Overview

Brought to you by YData

Dataset statistics

Number of variables42
Number of observations8760
Missing cells36886
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.2 MiB
Average record size in memory2.9 KiB

Variable types

Text13
Categorical13
Numeric16

Alerts

AGE is highly overall correlated with PROPERTY_TYPE and 2 other fieldsHigh correlation
AREA is highly overall correlated with BEDROOM_NUM and 4 other fieldsHigh correlation
AmenityScore is highly overall correlated with FACING and 1 other fieldsHigh correlation
BALCONY_NUM is highly overall correlated with TRANSACT_TYPEHigh correlation
BEDROOM_NUM is highly overall correlated with AREA and 4 other fieldsHigh correlation
BUILTUP_SQFT is highly overall correlated with AREA and 6 other fieldsHigh correlation
CARPET_SQFT is highly overall correlated with AREA and 3 other fieldsHigh correlation
CITY is highly overall correlated with city and 1 other fieldsHigh correlation
FACING is highly overall correlated with AmenityScore and 1 other fieldsHigh correlation
FLOOR_NUM is highly overall correlated with AmenityScore and 1 other fieldsHigh correlation
FURNISH is highly overall correlated with TRANSACT_TYPEHigh correlation
OWNTYPE is highly overall correlated with PREFERENCE and 2 other fieldsHigh correlation
PREFERENCE is highly overall correlated with OWNTYPE and 2 other fieldsHigh correlation
PRICE_SQFT is highly overall correlated with PriceHigh correlation
PROPERTY_TYPE is highly overall correlated with AGE and 1 other fieldsHigh correlation
Price is highly overall correlated with AREA and 4 other fieldsHigh correlation
SUPERAREA_UNIT is highly overall correlated with BUILTUP_SQFT and 1 other fieldsHigh correlation
SUPERBUILTUP_SQFT is highly overall correlated with AREA and 4 other fieldsHigh correlation
TRANSACT_TYPE is highly overall correlated with AGE and 6 other fieldsHigh correlation
city is highly overall correlated with CITY and 1 other fieldsHigh correlation
preference is highly overall correlated with OWNTYPE and 2 other fieldsHigh correlation
side is highly overall correlated with CITY and 1 other fieldsHigh correlation
society is highly overall correlated with BUILTUP_SQFTHigh correlation
type is highly overall correlated with AGE and 1 other fieldsHigh correlation
PROPERTY_TYPE is highly imbalanced (59.7%) Imbalance
OWNTYPE is highly imbalanced (62.2%) Imbalance
SUPERAREA_UNIT is highly imbalanced (63.6%) Imbalance
type is highly imbalanced (59.7%) Imbalance
BEDROOM_NUM has 482 (5.5%) missing values Missing
PROP_NAME has 631 (7.2%) missing values Missing
TOTAL_LANDMARK_COUNT has 223 (2.5%) missing values Missing
FORMATTED_LANDMARK_DETAILS has 223 (2.5%) missing values Missing
SOCIETY_NAME has 631 (7.2%) missing values Missing
BUILDING_NAME has 644 (7.4%) missing values Missing
CARPET_SQFT has 6278 (71.7%) missing values Missing
SUPERBUILTUP_SQFT has 3379 (38.6%) missing values Missing
BUILTUP_SQFT has 7086 (80.9%) missing values Missing
SUPER_AREA has 7944 (90.7%) missing values Missing
SUPERAREA_UNIT has 7944 (90.7%) missing values Missing
society has 1370 (15.6%) missing values Missing
PRICE_SQFT is highly skewed (γ1 = 49.52797639) Skewed
AREA is highly skewed (γ1 = 54.49675322) Skewed
CARPET_SQFT is highly skewed (γ1 = 41.89793539) Skewed
SUPERBUILTUP_SQFT is highly skewed (γ1 = 52.26555203) Skewed
BUILTUP_SQFT is highly skewed (γ1 = 32.91587097) Skewed
SUPER_AREA is highly skewed (γ1 = 28.29620787) Skewed
PROP_ID has unique values Unique
FACING has 2668 (30.5%) zeros Zeros
AGE has 479 (5.5%) zeros Zeros
TOTAL_FLOOR has 102 (1.2%) zeros Zeros
FLOOR_NUM has 1209 (13.8%) zeros Zeros
AmenityScore has 2231 (25.5%) zeros Zeros

Reproduction

Analysis started2025-07-24 18:40:30.597694
Analysis finished2025-07-24 18:41:18.864289
Duration48.27 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

PROP_ID
Text

Unique 

Distinct8760
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size564.7 KiB
2025-07-24T18:41:19.157520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.9997717
Min length8

Characters and Unicode

Total characters78838
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8760 ?
Unique (%)100.0%

Sample

1st rowY71306776
2nd rowB70113976
3rd rowO70374510
4th rowQ69170182
5th rowF69917588
ValueCountFrequency (%)
k71064488 1
 
< 0.1%
e67621228 1
 
< 0.1%
y71306776 1
 
< 0.1%
b70113976 1
 
< 0.1%
o70374510 1
 
< 0.1%
q69170182 1
 
< 0.1%
f69917588 1
 
< 0.1%
y69917586 1
 
< 0.1%
u71217472 1
 
< 0.1%
u71215326 1
 
< 0.1%
Other values (8750) 8750
99.9%
2025-07-24T18:41:19.572809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 10768
13.7%
0 9418
11.9%
6 8075
10.2%
1 7807
9.9%
2 6561
8.3%
8 6420
8.1%
4 6342
8.0%
9 5327
6.8%
3 4773
6.1%
5 4587
5.8%
Other values (26) 8760
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70078
88.9%
Uppercase Letter 8760
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 379
 
4.3%
C 374
 
4.3%
D 368
 
4.2%
B 366
 
4.2%
O 366
 
4.2%
M 363
 
4.1%
V 351
 
4.0%
Q 348
 
4.0%
R 346
 
3.9%
H 346
 
3.9%
Other values (16) 5153
58.8%
Decimal Number
ValueCountFrequency (%)
7 10768
15.4%
0 9418
13.4%
6 8075
11.5%
1 7807
11.1%
2 6561
9.4%
8 6420
9.2%
4 6342
9.0%
9 5327
7.6%
3 4773
6.8%
5 4587
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 70078
88.9%
Latin 8760
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 379
 
4.3%
C 374
 
4.3%
D 368
 
4.2%
B 366
 
4.2%
O 366
 
4.2%
M 363
 
4.1%
V 351
 
4.0%
Q 348
 
4.0%
R 346
 
3.9%
H 346
 
3.9%
Other values (16) 5153
58.8%
Common
ValueCountFrequency (%)
7 10768
15.4%
0 9418
13.4%
6 8075
11.5%
1 7807
11.1%
2 6561
9.4%
8 6420
9.2%
4 6342
9.0%
9 5327
7.6%
3 4773
6.8%
5 4587
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 10768
13.7%
0 9418
11.9%
6 8075
10.2%
1 7807
9.9%
2 6561
8.3%
8 6420
8.1%
4 6342
8.0%
9 5327
6.8%
3 4773
6.1%
5 4587
5.8%
Other values (26) 8760
11.1%

PREFERENCE
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size496.3 KiB
S
7026 
R
1352 
P
 
382

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8760
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 7026
80.2%
R 1352
 
15.4%
P 382
 
4.4%

Length

2025-07-24T18:41:19.680675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:19.745475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s 7026
80.2%
r 1352
 
15.4%
p 382
 
4.4%

Most occurring characters

ValueCountFrequency (%)
S 7026
80.2%
R 1352
 
15.4%
P 382
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8760
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 7026
80.2%
R 1352
 
15.4%
P 382
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 8760
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 7026
80.2%
R 1352
 
15.4%
P 382
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 7026
80.2%
R 1352
 
15.4%
P 382
 
4.4%
Distinct8252
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-07-24T18:41:20.048924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4452
Median length1237
Mean length577.53836
Min length30

Characters and Unicode

Total characters5059236
Distinct characters94
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8049 ?
Unique (%)91.9%

Sample

1st rowBook your 2 BHK flat in Srijan Star Swapno Puron, Amtala , Kolkata South. Having a super built-up area of 518.0 sq. ft. - 623.0 sq. ft., these flats promise an exclusive view and refreshing vibes, making them well ventilated. It is a superbly design project set amidst excellent surroundings and offers residents a world-class infrastructure. Look at its range of general amenities to premium amenities that include Car Parking, Cafeteria, 24/7 Power Backup, Senior Citizen Sitout, Carrom and what not. Now, you can buy this exclusive 2 BHK flat at a price range of Rs. 19.1 Lac - Rs. 22.67 Lac.
2nd rowMake Natural Quest your next home. This project offers 3 BHK flats in EM Bypass, Kolkata South. With a built-up area ranging from 1110.0 sq. ft. to 1121.0 sq. ft., these flats combine the finest design and amenities in Kolkata South to provide a living experience unlike any other. It is a new launch project and is unique in its perfect harmony of classic form and modern construction. The features and the amenities like Car Parking, Paved Compound, CCTV Camera Security, Pergola, Reflexology Park and many more make this residential project an epitome of modern living. Further, the society is well connected with all means of public transport. The flats are available at a price range of Rs. 1.17 Crore to Rs. 1.18 Crore.
3rd rowBook your 3 BHK apartment in Garia, Kolkata South at a price ranging from Rs. 1.22 Crore to Rs. 1.35 Crore. The Ganguly 4Sight Eminence hosts exclusively designed towers, each presenting an epitome of class and simplicity. The residential apartments have a super built-up area of 1376.0 sq. ft. to 1516.0 sq. ft. and are a new launch. With an impressive layout and a comprehensive range of amenities, like , etc., the Ganguly 4Sight Eminence leaves no stone unturned to amaze.
4th rowDev bhumi in joka, kolkata south by ocean land developer is a residential project. Dev bhumi price ranges from 1.49 cr to 5.99 cr. It also offers car parking. The project is spread over a total area of 2 acres of land. It has 50% of open space. An accommodation of 500 units has been provided. Dev bhumi brochure is also available for easy reference. About city: Kolkata, the city of joy has a realty market scenario like no other. The addition of emerging townships in the city has affected the residential real estate market hugely. Along with this, the developing infrastructure, presence of large industries, well connectivity between major micro markets and a stable economy of the state has helped kolkata create a positive feel throughout.
5th rowLet your dream of owning a flat come true with DTC Sojon. It offers an exclusive range of 3 BHK flats in Joka, Kolkata South. They are available at price of Rs. 60 Lac - Rs. 75 Lac and have a super built-up area of 1130.0 sq. ft. to 1460.0 sq. ft. The project is a secured gated community that further has 24x7 security systems. It has round the clock power back up as well as features many more attractive facilities with a host of amenities like Senior Citizen Sitout, CCTV Camera Security, 24/7 Water Supply, Toddler Pool, Reflexology Park, etc.
ValueCountFrequency (%)
the 32544
 
3.8%
is 29509
 
3.4%
of 25084
 
2.9%
a 23794
 
2.8%
and 19150
 
2.2%
this 17794
 
2.1%
in 16841
 
1.9%
to 15014
 
1.7%
flat 14837
 
1.7%
for 13342
 
1.5%
Other values (13538) 656178
75.9%
2025-07-24T18:41:20.549962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
844282
16.7%
e 392108
 
7.8%
a 363024
 
7.2%
t 335672
 
6.6%
o 306986
 
6.1%
i 293156
 
5.8%
s 266055
 
5.3%
r 250797
 
5.0%
n 234112
 
4.6%
l 196373
 
3.9%
Other values (84) 1576671
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3790452
74.9%
Space Separator 844425
 
16.7%
Other Punctuation 144535
 
2.9%
Decimal Number 118427
 
2.3%
Uppercase Letter 114134
 
2.3%
Control 23802
 
0.5%
Dash Punctuation 11769
 
0.2%
Close Punctuation 6088
 
0.1%
Open Punctuation 4991
 
0.1%
Math Symbol 576
 
< 0.1%
Other values (3) 37
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 392108
 
10.3%
a 363024
 
9.6%
t 335672
 
8.9%
o 306986
 
8.1%
i 293156
 
7.7%
s 266055
 
7.0%
r 250797
 
6.6%
n 234112
 
6.2%
l 196373
 
5.2%
h 136363
 
3.6%
Other values (16) 1015806
26.8%
Uppercase Letter
ValueCountFrequency (%)
T 27154
23.8%
F 8828
 
7.7%
A 8313
 
7.3%
C 7186
 
6.3%
S 6707
 
5.9%
B 5837
 
5.1%
I 5528
 
4.8%
L 5321
 
4.7%
R 4983
 
4.4%
P 4951
 
4.3%
Other values (16) 29326
25.7%
Other Punctuation
ValueCountFrequency (%)
. 80961
56.0%
, 49865
34.5%
/ 5079
 
3.5%
: 3633
 
2.5%
& 2051
 
1.4%
' 1645
 
1.1%
? 437
 
0.3%
* 359
 
0.2%
! 230
 
0.2%
% 181
 
0.1%
Other values (4) 94
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 25616
21.6%
0 21272
18.0%
2 20863
17.6%
3 13236
11.2%
5 10177
 
8.6%
4 9548
 
8.1%
7 5202
 
4.4%
6 4642
 
3.9%
8 4270
 
3.6%
9 3601
 
3.0%
Math Symbol
ValueCountFrequency (%)
+ 392
68.1%
| 140
 
24.3%
= 29
 
5.0%
~ 11
 
1.9%
> 3
 
0.5%
< 1
 
0.2%
Space Separator
ValueCountFrequency (%)
844282
> 99.9%
  143
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 11626
98.8%
143
 
1.2%
Close Punctuation
ValueCountFrequency (%)
) 6087
> 99.9%
} 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 4990
> 99.9%
{ 1
 
< 0.1%
Control
ValueCountFrequency (%)
23802
100.0%
Final Punctuation
ValueCountFrequency (%)
30
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3904586
77.2%
Common 1154650
 
22.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 392108
 
10.0%
a 363024
 
9.3%
t 335672
 
8.6%
o 306986
 
7.9%
i 293156
 
7.5%
s 266055
 
6.8%
r 250797
 
6.4%
n 234112
 
6.0%
l 196373
 
5.0%
h 136363
 
3.5%
Other values (42) 1129940
28.9%
Common
ValueCountFrequency (%)
844282
73.1%
. 80961
 
7.0%
, 49865
 
4.3%
1 25616
 
2.2%
23802
 
2.1%
0 21272
 
1.8%
2 20863
 
1.8%
3 13236
 
1.1%
- 11626
 
1.0%
5 10177
 
0.9%
Other values (32) 52950
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5058920
> 99.9%
Punctuation 173
 
< 0.1%
None 143
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
844282
16.7%
e 392108
 
7.8%
a 363024
 
7.2%
t 335672
 
6.6%
o 306986
 
6.1%
i 293156
 
5.8%
s 266055
 
5.3%
r 250797
 
5.0%
n 234112
 
4.6%
l 196373
 
3.9%
Other values (81) 1576355
31.2%
None
ValueCountFrequency (%)
  143
100.0%
Punctuation
ValueCountFrequency (%)
143
82.7%
30
 
17.3%

PROPERTY_TYPE
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size667.1 KiB
Residential Apartment
7518 
Independent House/Villa
 
497
Residential Land
 
476
Independent/Builder Floor
 
269

Length

Max length25
Median length21
Mean length20.964612
Min length16

Characters and Unicode

Total characters183650
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResidential Apartment
2nd rowResidential Apartment
3rd rowResidential Apartment
4th rowResidential Land
5th rowResidential Apartment

Common Values

ValueCountFrequency (%)
Residential Apartment 7518
85.8%
Independent House/Villa 497
 
5.7%
Residential Land 476
 
5.4%
Independent/Builder Floor 269
 
3.1%

Length

2025-07-24T18:41:20.667066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:20.746455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
residential 7994
45.6%
apartment 7518
42.9%
independent 497
 
2.8%
house/villa 497
 
2.8%
land 476
 
2.7%
independent/builder 269
 
1.5%
floor 269
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 26570
14.5%
t 23796
13.0%
n 18286
10.0%
i 16754
9.1%
a 16485
9.0%
d 10271
 
5.6%
l 9526
 
5.2%
8760
 
4.8%
s 8491
 
4.6%
p 8284
 
4.5%
Other values (13) 36427
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 155838
84.9%
Uppercase Letter 18286
 
10.0%
Space Separator 8760
 
4.8%
Other Punctuation 766
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 26570
17.0%
t 23796
15.3%
n 18286
11.7%
i 16754
10.8%
a 16485
10.6%
d 10271
 
6.6%
l 9526
 
6.1%
s 8491
 
5.4%
p 8284
 
5.3%
r 8056
 
5.2%
Other values (3) 9319
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
R 7994
43.7%
A 7518
41.1%
I 766
 
4.2%
H 497
 
2.7%
V 497
 
2.7%
L 476
 
2.6%
B 269
 
1.5%
F 269
 
1.5%
Space Separator
ValueCountFrequency (%)
8760
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 174124
94.8%
Common 9526
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 26570
15.3%
t 23796
13.7%
n 18286
10.5%
i 16754
9.6%
a 16485
9.5%
d 10271
 
5.9%
l 9526
 
5.5%
s 8491
 
4.9%
p 8284
 
4.8%
r 8056
 
4.6%
Other values (11) 27605
15.9%
Common
ValueCountFrequency (%)
8760
92.0%
/ 766
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 183650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 26570
14.5%
t 23796
13.0%
n 18286
10.0%
i 16754
9.1%
a 16485
9.0%
d 10271
 
5.6%
l 9526
 
5.2%
8760
 
4.8%
s 8491
 
4.6%
p 8284
 
4.5%
Other values (13) 36427
19.8%

CITY
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size596.1 KiB
Kolkata South
3784 
Kolkata East
2872 
Kolkata North
1789 
Kolkata West
 
240
Kolkata Central
 
75

Length

Max length15
Median length13
Mean length12.661872
Min length12

Characters and Unicode

Total characters110918
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKolkata South
2nd rowKolkata South
3rd rowKolkata South
4th rowKolkata South
5th rowKolkata South

Common Values

ValueCountFrequency (%)
Kolkata South 3784
43.2%
Kolkata East 2872
32.8%
Kolkata North 1789
20.4%
Kolkata West 240
 
2.7%
Kolkata Central 75
 
0.9%

Length

2025-07-24T18:41:20.845850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:20.923939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
kolkata 8760
50.0%
south 3784
21.6%
east 2872
 
16.4%
north 1789
 
10.2%
west 240
 
1.4%
central 75
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 20467
18.5%
t 17520
15.8%
o 14333
12.9%
l 8835
8.0%
K 8760
7.9%
k 8760
7.9%
8760
7.9%
h 5573
 
5.0%
S 3784
 
3.4%
u 3784
 
3.4%
Other values (8) 10342
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 84638
76.3%
Uppercase Letter 17520
 
15.8%
Space Separator 8760
 
7.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 20467
24.2%
t 17520
20.7%
o 14333
16.9%
l 8835
10.4%
k 8760
10.3%
h 5573
 
6.6%
u 3784
 
4.5%
s 3112
 
3.7%
r 1864
 
2.2%
e 315
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
K 8760
50.0%
S 3784
21.6%
E 2872
 
16.4%
N 1789
 
10.2%
W 240
 
1.4%
C 75
 
0.4%
Space Separator
ValueCountFrequency (%)
8760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102158
92.1%
Common 8760
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 20467
20.0%
t 17520
17.1%
o 14333
14.0%
l 8835
8.6%
K 8760
8.6%
k 8760
8.6%
h 5573
 
5.5%
S 3784
 
3.7%
u 3784
 
3.7%
s 3112
 
3.0%
Other values (7) 7230
 
7.1%
Common
ValueCountFrequency (%)
8760
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 20467
18.5%
t 17520
15.8%
o 14333
12.9%
l 8835
8.0%
K 8760
7.9%
k 8760
7.9%
8760
7.9%
h 5573
 
5.0%
S 3784
 
3.4%
u 3784
 
3.4%
Other values (8) 10342
9.3%

TRANSACT_TYPE
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
1.0
5371 
0.0
1736 
2.0
1653 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26280
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 5371
61.3%
0.0 1736
 
19.8%
2.0 1653
 
18.9%

Length

2025-07-24T18:41:21.022633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:21.086403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5371
61.3%
0.0 1736
 
19.8%
2.0 1653
 
18.9%

Most occurring characters

ValueCountFrequency (%)
0 10496
39.9%
. 8760
33.3%
1 5371
20.4%
2 1653
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17520
66.7%
Other Punctuation 8760
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10496
59.9%
1 5371
30.7%
2 1653
 
9.4%
Other Punctuation
ValueCountFrequency (%)
. 8760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26280
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10496
39.9%
. 8760
33.3%
1 5371
20.4%
2 1653
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10496
39.9%
. 8760
33.3%
1 5371
20.4%
2 1653
 
6.3%

OWNTYPE
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size496.3 KiB
1
6849 
0
1734 
3
 
108
2
 
57
4
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8760
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6849
78.2%
0 1734
 
19.8%
3 108
 
1.2%
2 57
 
0.7%
4 12
 
0.1%

Length

2025-07-24T18:41:21.168908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:21.255067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 6849
78.2%
0 1734
 
19.8%
3 108
 
1.2%
2 57
 
0.7%
4 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 6849
78.2%
0 1734
 
19.8%
3 108
 
1.2%
2 57
 
0.7%
4 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8760
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6849
78.2%
0 1734
 
19.8%
3 108
 
1.2%
2 57
 
0.7%
4 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6849
78.2%
0 1734
 
19.8%
3 108
 
1.2%
2 57
 
0.7%
4 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6849
78.2%
0 1734
 
19.8%
3 108
 
1.2%
2 57
 
0.7%
4 12
 
0.1%

BEDROOM_NUM
Real number (ℝ)

High correlation  Missing 

Distinct17
Distinct (%)0.2%
Missing482
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean2.6891761
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:21.337033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum39
Range38
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1120204
Coefficient of variation (CV)0.41351713
Kurtosis176.89027
Mean2.6891761
Median Absolute Deviation (MAD)1
Skewness7.542423
Sum22261
Variance1.2365894
MonotonicityNot monotonic
2025-07-24T18:41:21.448408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 3631
41.4%
2 3159
36.1%
4 802
 
9.2%
1 480
 
5.5%
5 113
 
1.3%
6 31
 
0.4%
7 17
 
0.2%
8 14
 
0.2%
9 12
 
0.1%
10 7
 
0.1%
Other values (7) 12
 
0.1%
(Missing) 482
 
5.5%
ValueCountFrequency (%)
1 480
 
5.5%
2 3159
36.1%
3 3631
41.4%
4 802
 
9.2%
5 113
 
1.3%
6 31
 
0.4%
7 17
 
0.2%
8 14
 
0.2%
9 12
 
0.1%
10 7
 
0.1%
ValueCountFrequency (%)
39 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
16 2
 
< 0.1%
14 1
 
< 0.1%
12 5
 
0.1%
11 1
 
< 0.1%
10 7
0.1%
9 12
0.1%
8 14
0.2%

FURNISH
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size496.3 KiB
2
4362 
0
2366 
4
1175 
1
857 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8760
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 4362
49.8%
0 2366
27.0%
4 1175
 
13.4%
1 857
 
9.8%

Length

2025-07-24T18:41:21.557426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:21.634048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 4362
49.8%
0 2366
27.0%
4 1175
 
13.4%
1 857
 
9.8%

Most occurring characters

ValueCountFrequency (%)
2 4362
49.8%
0 2366
27.0%
4 1175
 
13.4%
1 857
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8760
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4362
49.8%
0 2366
27.0%
4 1175
 
13.4%
1 857
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 8760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4362
49.8%
0 2366
27.0%
4 1175
 
13.4%
1 857
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 4362
49.8%
0 2366
27.0%
4 1175
 
13.4%
1 857
 
9.8%

FACING
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3139269
Minimum0
Maximum8
Zeros2668
Zeros (%)30.5%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:21.713165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)7

Descriptive statistics

Standard deviation2.9504161
Coefficient of variation (CV)0.89030812
Kurtosis-1.5184784
Mean3.3139269
Median Absolute Deviation (MAD)3
Skewness0.25130842
Sum29030
Variance8.704955
MonotonicityNot monotonic
2025-07-24T18:41:21.802109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2668
30.5%
7 1811
20.7%
2 1260
14.4%
5 723
 
8.3%
3 686
 
7.8%
8 595
 
6.8%
1 450
 
5.1%
6 341
 
3.9%
4 226
 
2.6%
ValueCountFrequency (%)
0 2668
30.5%
1 450
 
5.1%
2 1260
14.4%
3 686
 
7.8%
4 226
 
2.6%
5 723
 
8.3%
6 341
 
3.9%
7 1811
20.7%
8 595
 
6.8%
ValueCountFrequency (%)
8 595
 
6.8%
7 1811
20.7%
6 341
 
3.9%
5 723
 
8.3%
4 226
 
2.6%
3 686
 
7.8%
2 1260
14.4%
1 450
 
5.1%
0 2668
30.5%

AGE
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9170091
Minimum0
Maximum6
Zeros479
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:21.883106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.054533
Coefficient of variation (CV)0.70432861
Kurtosis-1.5529758
Mean2.9170091
Median Absolute Deviation (MAD)1
Skewness0.27081967
Sum25553
Variance4.2211058
MonotonicityNot monotonic
2025-07-24T18:41:21.962692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 3039
34.7%
5 2149
24.5%
6 1205
 
13.8%
2 1125
 
12.8%
3 763
 
8.7%
0 479
 
5.5%
ValueCountFrequency (%)
0 479
 
5.5%
1 3039
34.7%
2 1125
 
12.8%
3 763
 
8.7%
5 2149
24.5%
6 1205
 
13.8%
ValueCountFrequency (%)
6 1205
 
13.8%
5 2149
24.5%
3 763
 
8.7%
2 1125
 
12.8%
1 3039
34.7%
0 479
 
5.5%

TOTAL_FLOOR
Real number (ℝ)

Zeros 

Distinct45
Distinct (%)0.5%
Missing51
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean9.9962108
Minimum0
Maximum81
Zeros102
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:22.076257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q315
95-th percentile28
Maximum81
Range81
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.7942782
Coefficient of variation (CV)0.87976118
Kurtosis1.950118
Mean9.9962108
Median Absolute Deviation (MAD)2
Skewness1.4176796
Sum87057
Variance77.339329
MonotonicityNot monotonic
2025-07-24T18:41:22.236563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
4 2294
26.2%
3 1005
 
11.5%
5 825
 
9.4%
20 329
 
3.8%
12 317
 
3.6%
2 294
 
3.4%
14 278
 
3.2%
11 245
 
2.8%
7 231
 
2.6%
6 228
 
2.6%
Other values (35) 2663
30.4%
ValueCountFrequency (%)
0 102
 
1.2%
1 122
 
1.4%
2 294
 
3.4%
3 1005
11.5%
4 2294
26.2%
5 825
 
9.4%
6 228
 
2.6%
7 231
 
2.6%
8 81
 
0.9%
9 89
 
1.0%
ValueCountFrequency (%)
81 1
 
< 0.1%
62 1
 
< 0.1%
46 6
 
0.1%
45 51
0.6%
42 3
 
< 0.1%
40 2
 
< 0.1%
38 4
 
< 0.1%
37 2
 
< 0.1%
36 1
 
< 0.1%
35 85
1.0%
Distinct2962
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Memory size652.3 KiB
2025-07-24T18:41:22.486001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length97
Median length81
Mean length19.239612
Min length1

Characters and Unicode

Total characters168539
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2440 ?
Unique (%)27.9%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 2146
 
24.5%
23,24,5,17,6,19,20,21 418
 
4.8%
5,17,21 137
 
1.6%
23 114
 
1.3%
21 111
 
1.3%
23,24,26,5 97
 
1.1%
24 91
 
1.0%
23,5,19,21 63
 
0.7%
23,24,17,6,19,20,21 61
 
0.7%
23,24,5,17,19,20,21 58
 
0.7%
Other values (2951) 5464
62.4%
2025-07-24T18:41:22.869462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 56123
33.3%
2 27290
16.2%
1 20206
 
12.0%
4 16785
 
10.0%
3 13457
 
8.0%
0 9121
 
5.4%
9 6899
 
4.1%
5 6627
 
3.9%
6 5468
 
3.2%
7 5246
 
3.1%
Other values (3) 1317
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112368
66.7%
Other Punctuation 56123
33.3%
Uppercase Letter 48
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 27290
24.3%
1 20206
18.0%
4 16785
14.9%
3 13457
12.0%
0 9121
 
8.1%
9 6899
 
6.1%
5 6627
 
5.9%
6 5468
 
4.9%
7 5246
 
4.7%
8 1269
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
F 35
72.9%
P 13
 
27.1%
Other Punctuation
ValueCountFrequency (%)
, 56123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 168491
> 99.9%
Latin 48
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
, 56123
33.3%
2 27290
16.2%
1 20206
 
12.0%
4 16785
 
10.0%
3 13457
 
8.0%
0 9121
 
5.4%
9 6899
 
4.1%
5 6627
 
3.9%
6 5468
 
3.2%
7 5246
 
3.1%
Latin
ValueCountFrequency (%)
F 35
72.9%
P 13
 
27.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 56123
33.3%
2 27290
16.2%
1 20206
 
12.0%
4 16785
 
10.0%
3 13457
 
8.0%
0 9121
 
5.4%
9 6899
 
4.1%
5 6627
 
3.9%
6 5468
 
3.2%
7 5246
 
3.1%
Other values (3) 1317
 
0.8%

PROP_NAME
Text

Missing 

Distinct2666
Distinct (%)32.8%
Missing631
Missing (%)7.2%
Memory size595.0 KiB
2025-07-24T18:41:23.184062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length40
Mean length15.447657
Min length2

Characters and Unicode

Total characters125574
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1758 ?
Unique (%)21.6%

Sample

1st rowSrijan Star Swapno Puron
2nd rowNatural Quest
3rd rowGanguly 4Sight Eminence
4th rowDev Bhumi
5th rowDTC Sojon
ValueCountFrequency (%)
on 820
 
4.3%
request 783
 
4.1%
apartment 697
 
3.6%
city 386
 
2.0%
the 300
 
1.6%
merlin 287
 
1.5%
siddha 250
 
1.3%
new 228
 
1.2%
garden 178
 
0.9%
heights 164
 
0.9%
Other values (2242) 15142
78.7%
2025-07-24T18:41:23.698039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13086
 
10.4%
11595
 
9.2%
e 10040
 
8.0%
n 8012
 
6.4%
i 7298
 
5.8%
r 6587
 
5.2%
t 6517
 
5.2%
o 4697
 
3.7%
s 4103
 
3.3%
l 3807
 
3.0%
Other values (61) 49832
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90992
72.5%
Uppercase Letter 21422
 
17.1%
Space Separator 11595
 
9.2%
Decimal Number 1077
 
0.9%
Other Punctuation 288
 
0.2%
Open Punctuation 78
 
0.1%
Close Punctuation 78
 
0.1%
Dash Punctuation 44
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13086
14.4%
e 10040
11.0%
n 8012
 
8.8%
i 7298
 
8.0%
r 6587
 
7.2%
t 6517
 
7.2%
o 4697
 
5.2%
s 4103
 
4.5%
l 3807
 
4.2%
h 3472
 
3.8%
Other values (16) 23373
25.7%
Uppercase Letter
ValueCountFrequency (%)
S 2872
13.4%
A 2478
 
11.6%
R 1645
 
7.7%
P 1233
 
5.8%
C 1189
 
5.6%
T 1179
 
5.5%
N 1170
 
5.5%
O 1117
 
5.2%
M 1040
 
4.9%
G 973
 
4.5%
Other values (16) 6526
30.5%
Decimal Number
ValueCountFrequency (%)
2 292
27.1%
1 289
26.8%
0 174
16.2%
7 72
 
6.7%
5 63
 
5.8%
6 49
 
4.5%
3 41
 
3.8%
4 34
 
3.2%
9 33
 
3.1%
8 30
 
2.8%
Other Punctuation
ValueCountFrequency (%)
, 145
50.3%
. 115
39.9%
/ 23
 
8.0%
' 4
 
1.4%
& 1
 
0.3%
Space Separator
ValueCountFrequency (%)
11595
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112414
89.5%
Common 13160
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13086
 
11.6%
e 10040
 
8.9%
n 8012
 
7.1%
i 7298
 
6.5%
r 6587
 
5.9%
t 6517
 
5.8%
o 4697
 
4.2%
s 4103
 
3.6%
l 3807
 
3.4%
h 3472
 
3.1%
Other values (42) 44795
39.8%
Common
ValueCountFrequency (%)
11595
88.1%
2 292
 
2.2%
1 289
 
2.2%
0 174
 
1.3%
, 145
 
1.1%
. 115
 
0.9%
( 78
 
0.6%
) 78
 
0.6%
7 72
 
0.5%
5 63
 
0.5%
Other values (9) 259
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13086
 
10.4%
11595
 
9.2%
e 10040
 
8.0%
n 8012
 
6.4%
i 7298
 
5.8%
r 6587
 
5.2%
t 6517
 
5.2%
o 4697
 
3.7%
s 4103
 
3.3%
l 3807
 
3.0%
Other values (61) 49832
39.7%

PRICE_SQFT
Real number (ℝ)

High correlation  Skewed 

Distinct3530
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120963.04
Minimum0
Maximum1.2 × 108
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:23.830834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q12555.5
median4621.5
Q37154.25
95-th percentile14331.5
Maximum1.2 × 108
Range1.2 × 108
Interquartile range (IQR)4598.75

Descriptive statistics

Standard deviation1614599.9
Coefficient of variation (CV)13.347878
Kurtosis3494.0612
Mean120963.04
Median Absolute Deviation (MAD)2329
Skewness49.527976
Sum1.0596362 × 109
Variance2.6069327 × 1012
MonotonicityNot monotonic
2025-07-24T18:41:23.964642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4500 109
 
1.2%
16 104
 
1.2%
4000 102
 
1.2%
15 93
 
1.1%
18 90
 
1.0%
20 86
 
1.0%
17 81
 
0.9%
12 81
 
0.9%
3500 71
 
0.8%
5000 67
 
0.8%
Other values (3520) 7876
89.9%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 25
0.3%
2 30
0.3%
3 27
0.3%
4 26
0.3%
5 34
0.4%
6 25
0.3%
7 31
0.4%
8 21
0.2%
9 22
0.3%
ValueCountFrequency (%)
120000000 1
< 0.1%
23125000 1
< 0.1%
20000000 2
< 0.1%
18303571 1
< 0.1%
18000000 1
< 0.1%
15000000 1
< 0.1%
14545454 1
< 0.1%
14285714 1
< 0.1%
14000000 1
< 0.1%
13750000 1
< 0.1%
Distinct2820
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Memory size928.5 KiB
2025-07-24T18:41:24.283865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length79
Median length68
Mean length51.52089
Min length39

Characters and Unicode

Total characters451323
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1826 ?
Unique (%)20.8%

Sample

1st row{'LATITUDE': '22.364447', 'LONGITUDE': '88.274642'}
2nd row{'LATITUDE': '22.518795', 'LONGITUDE': '88.388439'}
3rd row{'LATITUDE': '22.5137646', 'LONGITUDE': '88.3666797'}
4th row{'LATITUDE': '22.45383', 'LONGITUDE': '88.249572'}
5th row{'LATITUDE': '22.44213', 'LONGITUDE': '88.29551'}
ValueCountFrequency (%)
latitude 8760
25.0%
longitude 8760
25.0%
22.6006912 418
 
1.2%
88.4694535 418
 
1.2%
88.35469 160
 
0.5%
22.536025 160
 
0.5%
88.2978736068 148
 
0.4%
22.4439029807 148
 
0.4%
22.5 130
 
0.4%
88.35 129
 
0.4%
Other values (5550) 15809
45.1%
2025-07-24T18:41:24.757709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 70080
 
15.5%
2 26969
 
6.0%
8 26712
 
5.9%
26280
 
5.8%
T 26280
 
5.8%
E 17520
 
3.9%
L 17520
 
3.9%
: 17520
 
3.9%
D 17520
 
3.9%
U 17520
 
3.9%
Other values (17) 187402
41.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 148920
33.0%
Decimal Number 144723
32.1%
Other Punctuation 113880
25.2%
Space Separator 26280
 
5.8%
Open Punctuation 8760
 
1.9%
Close Punctuation 8760
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 26969
18.6%
8 26712
18.5%
4 15299
10.6%
5 12967
9.0%
3 12852
8.9%
6 12584
8.7%
9 10117
 
7.0%
1 9131
 
6.3%
7 9090
 
6.3%
0 9002
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
T 26280
17.6%
E 17520
11.8%
L 17520
11.8%
D 17520
11.8%
U 17520
11.8%
I 17520
11.8%
A 8760
 
5.9%
O 8760
 
5.9%
G 8760
 
5.9%
N 8760
 
5.9%
Other Punctuation
ValueCountFrequency (%)
' 70080
61.5%
: 17520
 
15.4%
. 17520
 
15.4%
, 8760
 
7.7%
Space Separator
ValueCountFrequency (%)
26280
100.0%
Open Punctuation
ValueCountFrequency (%)
{ 8760
100.0%
Close Punctuation
ValueCountFrequency (%)
} 8760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 302403
67.0%
Latin 148920
33.0%

Most frequent character per script

Common
ValueCountFrequency (%)
' 70080
23.2%
2 26969
 
8.9%
8 26712
 
8.8%
26280
 
8.7%
: 17520
 
5.8%
. 17520
 
5.8%
4 15299
 
5.1%
5 12967
 
4.3%
3 12852
 
4.2%
6 12584
 
4.2%
Other values (7) 63620
21.0%
Latin
ValueCountFrequency (%)
T 26280
17.6%
E 17520
11.8%
L 17520
11.8%
D 17520
11.8%
U 17520
11.8%
I 17520
11.8%
A 8760
 
5.9%
O 8760
 
5.9%
G 8760
 
5.9%
N 8760
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 451323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 70080
 
15.5%
2 26969
 
6.0%
8 26712
 
5.9%
26280
 
5.8%
T 26280
 
5.8%
E 17520
 
3.9%
L 17520
 
3.9%
: 17520
 
3.9%
D 17520
 
3.9%
U 17520
 
3.9%
Other values (17) 187402
41.5%
Distinct3462
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Memory size680.5 KiB
2025-07-24T18:41:24.990600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length98
Median length86
Mean length22.529452
Min length1

Characters and Unicode

Total characters197358
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2831 ?
Unique (%)32.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 2032
 
23.2%
5,17,20,21,23,24,6,19,101,102 171
 
2.0%
5,17,21,101,102 129
 
1.5%
103 119
 
1.4%
23 107
 
1.2%
5,17,20,21,23,24,6,19 98
 
1.1%
5,23,24,26 97
 
1.1%
5,17,20,21,23,24,6,19,101,102,103 83
 
0.9%
21 76
 
0.9%
24 66
 
0.8%
Other values (3452) 5782
66.0%
2025-07-24T18:41:25.350427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 63115
32.0%
2 30381
15.4%
1 30050
15.2%
4 16787
 
8.5%
0 16391
 
8.3%
3 15030
 
7.6%
9 6890
 
3.5%
5 6729
 
3.4%
6 5480
 
2.8%
7 5238
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134243
68.0%
Other Punctuation 63115
32.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 30381
22.6%
1 30050
22.4%
4 16787
12.5%
0 16391
12.2%
3 15030
11.2%
9 6890
 
5.1%
5 6729
 
5.0%
6 5480
 
4.1%
7 5238
 
3.9%
8 1267
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 63115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 197358
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 63115
32.0%
2 30381
15.4%
1 30050
15.2%
4 16787
 
8.5%
0 16391
 
8.3%
3 15030
 
7.6%
9 6890
 
3.5%
5 6729
 
3.4%
6 5480
 
2.8%
7 5238
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 63115
32.0%
2 30381
15.4%
1 30050
15.2%
4 16787
 
8.5%
0 16391
 
8.3%
3 15030
 
7.6%
9 6890
 
3.5%
5 6729
 
3.4%
6 5480
 
2.8%
7 5238
 
2.7%

AREA
Real number (ℝ)

High correlation  Skewed 

Distinct1824
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2744.8582
Minimum1
Maximum4356000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:25.490626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile540
Q1856
median1135
Q31500
95-th percentile2800
Maximum4356000
Range4355999
Interquartile range (IQR)644

Descriptive statistics

Standard deviation64477.04
Coefficient of variation (CV)23.490117
Kurtosis3200.9018
Mean2744.8582
Median Absolute Deviation (MAD)310
Skewness54.496753
Sum24044958
Variance4.1572886 × 109
MonotonicityNot monotonic
2025-07-24T18:41:25.633347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 134
 
1.5%
1200 118
 
1.3%
1250 118
 
1.3%
850 113
 
1.3%
900 109
 
1.2%
800 105
 
1.2%
1100 102
 
1.2%
750 84
 
1.0%
1440 78
 
0.9%
1500 69
 
0.8%
Other values (1814) 7730
88.2%
ValueCountFrequency (%)
1 3
< 0.1%
2 2
< 0.1%
3 3
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
14 1
 
< 0.1%
40 1
 
< 0.1%
75 2
< 0.1%
80 1
 
< 0.1%
ValueCountFrequency (%)
4356000 1
< 0.1%
2994000 1
< 0.1%
2581176 1
< 0.1%
900000 1
< 0.1%
871199 1
< 0.1%
519119 1
< 0.1%
100799 1
< 0.1%
52271 1
< 0.1%
45360 1
< 0.1%
40319 1
< 0.1%
Distinct1492
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size702.3 KiB
2025-07-24T18:41:25.939915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length89
Median length73
Mean length25.079452
Min length13

Characters and Unicode

Total characters219696
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique890 ?
Unique (%)10.2%

Sample

1st row2 BHK Flat in Amtala
2nd row3 BHK Flat in EM Bypass
3rd row3 BHK Flat in Garia
4th rowResidential land / Plot in Joka
5th row3 BHK Flat in Joka
ValueCountFrequency (%)
in 8760
17.5%
bhk 7782
15.6%
flat 7518
15.0%
3 3763
 
7.5%
2 3295
 
6.6%
new 1545
 
3.1%
town 1476
 
3.0%
4 802
 
1.6%
rajarhat 660
 
1.3%
1 637
 
1.3%
Other values (657) 13746
27.5%
2025-07-24T18:41:26.425192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41224
18.8%
a 21478
 
9.8%
n 15045
 
6.8%
i 12463
 
5.7%
l 11934
 
5.4%
t 11449
 
5.2%
B 10146
 
4.6%
H 8424
 
3.8%
K 8349
 
3.8%
e 7909
 
3.6%
Other values (55) 71275
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 121410
55.3%
Uppercase Letter 47660
 
21.7%
Space Separator 41224
 
18.8%
Decimal Number 8878
 
4.0%
Other Punctuation 522
 
0.2%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 21478
17.7%
n 15045
12.4%
i 12463
10.3%
l 11934
9.8%
t 11449
9.4%
e 7909
 
6.5%
r 7333
 
6.0%
o 7057
 
5.8%
d 3895
 
3.2%
u 3582
 
3.0%
Other values (16) 19265
15.9%
Uppercase Letter
ValueCountFrequency (%)
B 10146
21.3%
H 8424
17.7%
K 8349
17.5%
F 7793
16.4%
T 2181
 
4.6%
N 2103
 
4.4%
R 1573
 
3.3%
A 1316
 
2.8%
P 1151
 
2.4%
M 872
 
1.8%
Other values (14) 3752
 
7.9%
Decimal Number
ValueCountFrequency (%)
3 3771
42.5%
2 3367
37.9%
4 805
 
9.1%
1 713
 
8.0%
5 120
 
1.4%
6 34
 
0.4%
7 22
 
0.2%
9 16
 
0.2%
0 16
 
0.2%
8 14
 
0.2%
Other Punctuation
ValueCountFrequency (%)
/ 482
92.3%
, 23
 
4.4%
. 17
 
3.3%
Space Separator
ValueCountFrequency (%)
41224
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 169070
77.0%
Common 50626
 
23.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 21478
 
12.7%
n 15045
 
8.9%
i 12463
 
7.4%
l 11934
 
7.1%
t 11449
 
6.8%
B 10146
 
6.0%
H 8424
 
5.0%
K 8349
 
4.9%
e 7909
 
4.7%
F 7793
 
4.6%
Other values (40) 54080
32.0%
Common
ValueCountFrequency (%)
41224
81.4%
3 3771
 
7.4%
2 3367
 
6.7%
4 805
 
1.6%
1 713
 
1.4%
/ 482
 
1.0%
5 120
 
0.2%
6 34
 
0.1%
, 23
 
< 0.1%
7 22
 
< 0.1%
Other values (5) 65
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 219696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41224
18.8%
a 21478
 
9.8%
n 15045
 
6.8%
i 12463
 
5.7%
l 11934
 
5.4%
t 11449
 
5.2%
B 10146
 
4.6%
H 8424
 
3.8%
K 8349
 
3.8%
e 7909
 
3.6%
Other values (55) 71275
32.4%
Distinct110
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size794.7 KiB
2025-07-24T18:41:26.612513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length111
Median length107
Mean length35.877511
Min length2

Characters and Unicode

Total characters314287
Distinct characters36
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)0.4%

Sample

1st row['UNDER CONSTRUCTION', 'NEW BOOKING', 'RERA | HIRA']
2nd row['UNDER CONSTRUCTION', 'NEW LAUNCH', 'NEW BOOKING', 'RERA | HIRA']
3rd row['UNDER CONSTRUCTION', 'NEW LAUNCH', 'NEW BOOKING', 'RERA | HIRA']
4th row['READY TO MOVE', 'NEW BOOKING']
5th row['UNDER CONSTRUCTION', 'NEW BOOKING']
ValueCountFrequency (%)
resale 5371
12.1%
ready 4803
10.8%
to 4803
10.8%
move 4803
10.8%
2571
 
5.8%
rera 2267
 
5.1%
hira 2267
 
5.1%
construction 2223
 
5.0%
under 2223
 
5.0%
new 2066
 
4.6%
Other values (40) 11132
25.0%
2025-07-24T18:41:27.048804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 40746
13.0%
35769
 
11.4%
E 32395
 
10.3%
R 25271
 
8.0%
O 21288
 
6.8%
A 16446
 
5.2%
N 16015
 
5.1%
, 11917
 
3.8%
T 10421
 
3.3%
S 10332
 
3.3%
Other values (26) 93687
29.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 205743
65.5%
Other Punctuation 52663
 
16.8%
Space Separator 35769
 
11.4%
Open Punctuation 8760
 
2.8%
Close Punctuation 8760
 
2.8%
Math Symbol 2267
 
0.7%
Decimal Number 294
 
0.1%
Dash Punctuation 31
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 32395
15.7%
R 25271
12.3%
O 21288
10.3%
A 16446
 
8.0%
N 16015
 
7.8%
T 10421
 
5.1%
S 10332
 
5.0%
I 9566
 
4.6%
D 8729
 
4.2%
L 7715
 
3.7%
Other values (13) 47565
23.1%
Decimal Number
ValueCountFrequency (%)
2 159
54.1%
3 78
26.5%
4 27
 
9.2%
5 24
 
8.2%
0 5
 
1.7%
6 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
' 40746
77.4%
, 11917
 
22.6%
Space Separator
ValueCountFrequency (%)
35769
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 8760
100.0%
Close Punctuation
ValueCountFrequency (%)
] 8760
100.0%
Math Symbol
ValueCountFrequency (%)
| 2267
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 205743
65.5%
Common 108544
34.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 32395
15.7%
R 25271
12.3%
O 21288
10.3%
A 16446
 
8.0%
N 16015
 
7.8%
T 10421
 
5.1%
S 10332
 
5.0%
I 9566
 
4.6%
D 8729
 
4.2%
L 7715
 
3.7%
Other values (13) 47565
23.1%
Common
ValueCountFrequency (%)
' 40746
37.5%
35769
33.0%
, 11917
 
11.0%
[ 8760
 
8.1%
] 8760
 
8.1%
| 2267
 
2.1%
2 159
 
0.1%
3 78
 
0.1%
- 31
 
< 0.1%
4 27
 
< 0.1%
Other values (3) 30
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 40746
13.0%
35769
 
11.4%
E 32395
 
10.3%
R 25271
 
8.0%
O 21288
 
6.8%
A 16446
 
5.2%
N 16015
 
5.1%
, 11917
 
3.8%
T 10421
 
3.3%
S 10332
 
3.3%
Other values (26) 93687
29.8%

TOTAL_LANDMARK_COUNT
Real number (ℝ)

Missing 

Distinct50
Distinct (%)0.6%
Missing223
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean27.516926
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:27.231560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q112
median25
Q347
95-th percentile50
Maximum50
Range49
Interquartile range (IQR)35

Descriptive statistics

Standard deviation16.582042
Coefficient of variation (CV)0.60261245
Kurtosis-1.5629779
Mean27.516926
Median Absolute Deviation (MAD)15
Skewness0.13967375
Sum234912
Variance274.96413
MonotonicityNot monotonic
2025-07-24T18:41:27.432207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 1245
 
14.2%
50 1150
 
13.1%
49 740
 
8.4%
8 545
 
6.2%
11 317
 
3.6%
35 302
 
3.4%
12 256
 
2.9%
41 217
 
2.5%
9 215
 
2.5%
10 214
 
2.4%
Other values (40) 3336
38.1%
(Missing) 223
 
2.5%
ValueCountFrequency (%)
1 71
 
0.8%
2 33
 
0.4%
3 154
 
1.8%
4 47
 
0.5%
5 62
 
0.7%
6 78
 
0.9%
7 181
 
2.1%
8 545
6.2%
9 215
 
2.5%
10 214
 
2.4%
ValueCountFrequency (%)
50 1150
13.1%
49 740
8.4%
48 196
 
2.2%
47 80
 
0.9%
46 80
 
0.9%
45 56
 
0.6%
44 34
 
0.4%
43 64
 
0.7%
42 123
 
1.4%
41 217
 
2.5%
Distinct1459
Distinct (%)17.1%
Missing223
Missing (%)2.5%
Memory size7.2 MiB
2025-07-24T18:41:27.748204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1649
Median length1245
Mean length824.22537
Min length2

Characters and Unicode

Total characters7036412
Distinct characters59
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique558 ?
Unique (%)6.5%

Sample

1st row[{'category': 'Shopping', 'text': '1 Shopping', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'Hospital', 'text': '1 Hospital', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}]
2nd row[{'category': 'MetroStation', 'text': '1 Metro Station', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'Shopping', 'text': '2 Shoppings', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'Education', 'text': '2 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'Hospital', 'text': '2 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Airport', 'text': '1 Airport', 'className': 'aprt', 'icon': 'https://static.99acres.com/universalapp/img/aprt.png'}, {'category': 'RailwayStation', 'text': '1 Railway Station', 'className': 'rail', 'icon': 'https://static.99acres.com/universalapp/img/rail.png'}]
3rd row[{'category': 'Shopping', 'text': '1 Shopping', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'Education', 'text': '2 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'Hospital', 'text': '1 Hospital', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Airport', 'text': '1 Airport', 'className': 'aprt', 'icon': 'https://static.99acres.com/universalapp/img/aprt.png'}, {'category': 'RailwayStation', 'text': '1 Railway Station', 'className': 'rail', 'icon': 'https://static.99acres.com/universalapp/img/rail.png'}]
4th row[{'category': 'MetroStation', 'text': '2 Metro Stations', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'Connectivity', 'text': '1 Connectivity', 'className': 'hw', 'icon': 'https://static.99acres.com/universalapp/img/hw.png'}, {'category': 'Education', 'text': '6 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'Hospital', 'text': '4 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}]
5th row[{'category': 'MetroStation', 'text': '1 Metro Station', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'Shopping', 'text': '1 Shopping', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'Connectivity', 'text': '1 Connectivity', 'className': 'hw', 'icon': 'https://static.99acres.com/universalapp/img/hw.png'}, {'category': 'Education', 'text': '2 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'Hospital', 'text': '1 Hospital', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Airport', 'text': '1 Airport', 'className': 'aprt', 'icon': 'https://static.99acres.com/universalapp/img/aprt.png'}, {'category': 'RailwayStation', 'text': '1 Railway Station', 'className': 'rail', 'icon': 'https://static.99acres.com/universalapp/img/rail.png'}, {'category': 'Hotel', 'text': '1 Hotels', 'className': 'htl', 'icon': 'https://static.99acres.com/universalapp/img/htl.png'}, {'category': 'GolfCourse', 'text': '1 Golf Course', 'className': 'golf', 'icon': 'https://static.99acres.com/universalapp/img/golf.png'}, {'category': 'Stadium', 'text': '1 Stadium', 'className': 'stdm', 'icon': 'https://static.99acres.com/universalapp/img/stdm.png'}]
ValueCountFrequency (%)
category 51048
 
10.7%
text 51048
 
10.7%
icon 51048
 
10.7%
classname 51048
 
10.7%
1 26486
 
5.6%
hospital 10058
 
2.1%
2 10039
 
2.1%
atm 9822
 
2.1%
https://static.99acres.com/universalapp/img/hsptl.png 8197
 
1.7%
hsptl 8197
 
1.7%
Other values (132) 199308
41.8%
2025-07-24T18:41:28.238263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 816768
 
11.6%
t 485614
 
6.9%
467762
 
6.6%
a 451056
 
6.4%
s 436670
 
6.2%
c 364346
 
5.2%
e 340897
 
4.8%
i 301970
 
4.3%
p 290672
 
4.1%
o 255490
 
3.6%
Other values (49) 2825167
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4406276
62.6%
Other Punctuation 1676075
 
23.8%
Space Separator 467762
 
6.6%
Uppercase Letter 204954
 
2.9%
Decimal Number 157642
 
2.2%
Open Punctuation 59585
 
0.8%
Close Punctuation 59585
 
0.8%
Connector Punctuation 4533
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 485614
11.0%
a 451056
10.2%
s 436670
9.9%
c 364346
 
8.3%
e 340897
 
7.7%
i 301970
 
6.9%
p 290672
 
6.6%
o 255490
 
5.8%
l 222783
 
5.1%
n 212208
 
4.8%
Other values (13) 1044570
23.7%
Uppercase Letter
ValueCountFrequency (%)
N 51048
24.9%
M 24546
12.0%
S 18460
 
9.0%
H 17906
 
8.7%
P 17836
 
8.7%
A 16600
 
8.1%
R 13296
 
6.5%
T 9066
 
4.4%
B 8730
 
4.3%
C 8650
 
4.2%
Other values (5) 18816
 
9.2%
Decimal Number
ValueCountFrequency (%)
9 102558
65.1%
1 30400
 
19.3%
2 11520
 
7.3%
3 4604
 
2.9%
4 2833
 
1.8%
5 1736
 
1.1%
6 1271
 
0.8%
7 1133
 
0.7%
0 872
 
0.6%
8 715
 
0.5%
Other Punctuation
ValueCountFrequency (%)
' 816768
48.7%
: 255240
 
15.2%
/ 255240
 
15.2%
, 195683
 
11.7%
. 153144
 
9.1%
Open Punctuation
ValueCountFrequency (%)
{ 51048
85.7%
[ 8537
 
14.3%
Close Punctuation
ValueCountFrequency (%)
} 51048
85.7%
] 8537
 
14.3%
Space Separator
ValueCountFrequency (%)
467762
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4533
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4611230
65.5%
Common 2425182
34.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 485614
 
10.5%
a 451056
 
9.8%
s 436670
 
9.5%
c 364346
 
7.9%
e 340897
 
7.4%
i 301970
 
6.5%
p 290672
 
6.3%
o 255490
 
5.5%
l 222783
 
4.8%
n 212208
 
4.6%
Other values (28) 1249524
27.1%
Common
ValueCountFrequency (%)
' 816768
33.7%
467762
19.3%
: 255240
 
10.5%
/ 255240
 
10.5%
, 195683
 
8.1%
. 153144
 
6.3%
9 102558
 
4.2%
{ 51048
 
2.1%
} 51048
 
2.1%
1 30400
 
1.3%
Other values (11) 46291
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7036412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 816768
 
11.6%
t 485614
 
6.9%
467762
 
6.6%
a 451056
 
6.4%
s 436670
 
6.2%
c 364346
 
5.2%
e 340897
 
4.8%
i 301970
 
4.3%
p 290672
 
4.1%
o 255490
 
3.6%
Other values (49) 2825167
40.2%

SOCIETY_NAME
Text

Missing 

Distinct2667
Distinct (%)32.8%
Missing631
Missing (%)7.2%
Memory size595.0 KiB
2025-07-24T18:41:28.698001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length40
Mean length15.447657
Min length2

Characters and Unicode

Total characters125574
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1762 ?
Unique (%)21.7%

Sample

1st rowSrijan Star Swapno Puron
2nd rowNatural Quest
3rd rowGanguly 4Sight Eminence
4th rowDev Bhumi
5th rowDTC Sojon
ValueCountFrequency (%)
on 820
 
4.3%
request 783
 
4.1%
apartment 697
 
3.6%
city 386
 
2.0%
the 300
 
1.6%
merlin 287
 
1.5%
siddha 250
 
1.3%
new 228
 
1.2%
garden 178
 
0.9%
heights 164
 
0.9%
Other values (2242) 15142
78.7%
2025-07-24T18:41:29.423043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13086
 
10.4%
11595
 
9.2%
e 10042
 
8.0%
n 8014
 
6.4%
i 7302
 
5.8%
r 6585
 
5.2%
t 6519
 
5.2%
o 4702
 
3.7%
s 4103
 
3.3%
l 3807
 
3.0%
Other values (61) 49819
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91014
72.5%
Uppercase Letter 21400
 
17.0%
Space Separator 11595
 
9.2%
Decimal Number 1077
 
0.9%
Other Punctuation 288
 
0.2%
Open Punctuation 78
 
0.1%
Close Punctuation 78
 
0.1%
Dash Punctuation 44
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13086
14.4%
e 10042
11.0%
n 8014
 
8.8%
i 7302
 
8.0%
r 6585
 
7.2%
t 6519
 
7.2%
o 4702
 
5.2%
s 4103
 
4.5%
l 3807
 
4.2%
h 3472
 
3.8%
Other values (16) 23382
25.7%
Uppercase Letter
ValueCountFrequency (%)
S 2872
13.4%
A 2478
 
11.6%
R 1647
 
7.7%
P 1233
 
5.8%
C 1188
 
5.6%
T 1177
 
5.5%
N 1168
 
5.5%
O 1112
 
5.2%
M 1040
 
4.9%
G 971
 
4.5%
Other values (16) 6514
30.4%
Decimal Number
ValueCountFrequency (%)
2 292
27.1%
1 289
26.8%
0 174
16.2%
7 72
 
6.7%
5 63
 
5.8%
6 49
 
4.5%
3 41
 
3.8%
4 34
 
3.2%
9 33
 
3.1%
8 30
 
2.8%
Other Punctuation
ValueCountFrequency (%)
, 145
50.3%
. 115
39.9%
/ 23
 
8.0%
' 4
 
1.4%
& 1
 
0.3%
Space Separator
ValueCountFrequency (%)
11595
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112414
89.5%
Common 13160
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13086
 
11.6%
e 10042
 
8.9%
n 8014
 
7.1%
i 7302
 
6.5%
r 6585
 
5.9%
t 6519
 
5.8%
o 4702
 
4.2%
s 4103
 
3.6%
l 3807
 
3.4%
h 3472
 
3.1%
Other values (42) 44782
39.8%
Common
ValueCountFrequency (%)
11595
88.1%
2 292
 
2.2%
1 289
 
2.2%
0 174
 
1.3%
, 145
 
1.1%
. 115
 
0.9%
( 78
 
0.6%
) 78
 
0.6%
7 72
 
0.5%
5 63
 
0.5%
Other values (9) 259
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13086
 
10.4%
11595
 
9.2%
e 10042
 
8.0%
n 8014
 
6.4%
i 7302
 
5.8%
r 6585
 
5.2%
t 6519
 
5.2%
o 4702
 
3.7%
s 4103
 
3.3%
l 3807
 
3.0%
Other values (61) 49819
39.7%

BUILDING_NAME
Text

Missing 

Distinct2662
Distinct (%)32.8%
Missing644
Missing (%)7.4%
Memory size594.5 KiB
2025-07-24T18:41:29.806897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length40
Mean length15.451331
Min length2

Characters and Unicode

Total characters125403
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1757 ?
Unique (%)21.6%

Sample

1st rowSrijan Star Swapno Puron
2nd rowNatural Quest
3rd rowGanguly 4Sight Eminence
4th rowDev Bhumi
5th rowDTC Sojon
ValueCountFrequency (%)
on 815
 
4.2%
request 778
 
4.1%
apartment 696
 
3.6%
city 386
 
2.0%
the 300
 
1.6%
merlin 287
 
1.5%
siddha 250
 
1.3%
new 227
 
1.2%
garden 178
 
0.9%
heights 164
 
0.9%
Other values (2239) 15125
78.8%
2025-07-24T18:41:30.275905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13076
 
10.4%
11578
 
9.2%
e 10025
 
8.0%
n 8004
 
6.4%
i 7296
 
5.8%
r 6579
 
5.2%
t 6507
 
5.2%
o 4692
 
3.7%
s 4097
 
3.3%
l 3807
 
3.0%
Other values (61) 49742
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90896
72.5%
Uppercase Letter 21373
 
17.0%
Space Separator 11578
 
9.2%
Decimal Number 1070
 
0.9%
Other Punctuation 286
 
0.2%
Open Punctuation 78
 
0.1%
Close Punctuation 78
 
0.1%
Dash Punctuation 44
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13076
14.4%
e 10025
11.0%
n 8004
 
8.8%
i 7296
 
8.0%
r 6579
 
7.2%
t 6507
 
7.2%
o 4692
 
5.2%
s 4097
 
4.5%
l 3807
 
4.2%
h 3469
 
3.8%
Other values (16) 23344
25.7%
Uppercase Letter
ValueCountFrequency (%)
S 2867
13.4%
A 2476
 
11.6%
R 1642
 
7.7%
P 1232
 
5.8%
C 1187
 
5.6%
T 1176
 
5.5%
N 1166
 
5.5%
O 1107
 
5.2%
M 1039
 
4.9%
G 971
 
4.5%
Other values (16) 6510
30.5%
Decimal Number
ValueCountFrequency (%)
2 292
27.3%
1 286
26.7%
0 171
16.0%
7 72
 
6.7%
5 63
 
5.9%
6 49
 
4.6%
3 41
 
3.8%
4 34
 
3.2%
9 33
 
3.1%
8 29
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 145
50.7%
. 113
39.5%
/ 23
 
8.0%
' 4
 
1.4%
& 1
 
0.3%
Space Separator
ValueCountFrequency (%)
11578
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112269
89.5%
Common 13134
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13076
 
11.6%
e 10025
 
8.9%
n 8004
 
7.1%
i 7296
 
6.5%
r 6579
 
5.9%
t 6507
 
5.8%
o 4692
 
4.2%
s 4097
 
3.6%
l 3807
 
3.4%
h 3469
 
3.1%
Other values (42) 44717
39.8%
Common
ValueCountFrequency (%)
11578
88.2%
2 292
 
2.2%
1 286
 
2.2%
0 171
 
1.3%
, 145
 
1.1%
. 113
 
0.9%
( 78
 
0.6%
) 78
 
0.6%
7 72
 
0.5%
5 63
 
0.5%
Other values (9) 258
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13076
 
10.4%
11578
 
9.2%
e 10025
 
8.0%
n 8004
 
6.4%
i 7296
 
5.8%
r 6579
 
5.2%
t 6507
 
5.2%
o 4692
 
3.7%
s 4097
 
3.3%
l 3807
 
3.0%
Other values (61) 49742
39.7%
Distinct2220
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-07-24T18:41:30.601757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length266
Median length204
Mean length118.71986
Min length108

Characters and Unicode

Total characters1039986
Distinct characters76
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1746 ?
Unique (%)19.9%

Sample

1st row{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '8005', 'LOCALITY_NAME': 'Amtala ', 'ADDRESS': None}
2nd row{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '603', 'LOCALITY_NAME': 'EM Bypass', 'ADDRESS': None}
3rd row{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '651', 'LOCALITY_NAME': 'Garia', 'ADDRESS': None}
4th row{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '844', 'LOCALITY_NAME': 'Joka', 'ADDRESS': None}
5th row{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '844', 'LOCALITY_NAME': 'Joka', 'ADDRESS': None}
ValueCountFrequency (%)
kolkata 8892
 
8.3%
city 8788
 
8.2%
address 8761
 
8.2%
locality_name 8760
 
8.2%
locality_id 8760
 
8.2%
city_name 8760
 
8.2%
none 6376
 
6.0%
south 3807
 
3.6%
27 3787
 
3.5%
east 2892
 
2.7%
Other values (2639) 37119
34.8%
2025-07-24T18:41:31.106456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 162446
 
15.6%
97942
 
9.4%
A 45725
 
4.4%
I 43917
 
4.2%
: 43839
 
4.2%
a 39364
 
3.8%
T 37494
 
3.6%
, 35813
 
3.4%
C 35781
 
3.4%
L 35395
 
3.4%
Other values (66) 462270
44.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 403563
38.8%
Other Punctuation 242629
23.3%
Lowercase Letter 196907
18.9%
Space Separator 97942
 
9.4%
Decimal Number 54930
 
5.3%
Connector Punctuation 26280
 
2.5%
Open Punctuation 8782
 
0.8%
Close Punctuation 8782
 
0.8%
Dash Punctuation 171
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 45725
11.3%
I 43917
10.9%
T 37494
9.3%
C 35781
8.9%
L 35395
8.8%
Y 35047
8.7%
E 29674
7.4%
N 28506
7.1%
D 26891
6.7%
S 22858
 
5.7%
Other values (16) 62275
15.4%
Lowercase Letter
ValueCountFrequency (%)
a 39364
20.0%
o 27846
14.1%
t 22331
11.3%
e 13951
 
7.1%
n 13041
 
6.6%
l 12881
 
6.5%
k 11252
 
5.7%
r 10816
 
5.5%
h 9146
 
4.6%
u 7599
 
3.9%
Other values (16) 28680
14.6%
Decimal Number
ValueCountFrequency (%)
2 11355
20.7%
8 6495
11.8%
1 5599
10.2%
7 5548
10.1%
5 5126
9.3%
4 4744
8.6%
6 4578
8.3%
0 4210
 
7.7%
3 3691
 
6.7%
9 3584
 
6.5%
Other Punctuation
ValueCountFrequency (%)
' 162446
67.0%
: 43839
 
18.1%
, 35813
 
14.8%
. 334
 
0.1%
/ 189
 
0.1%
" 4
 
< 0.1%
& 4
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
{ 8760
99.7%
( 22
 
0.3%
Close Punctuation
ValueCountFrequency (%)
} 8760
99.7%
) 22
 
0.3%
Space Separator
ValueCountFrequency (%)
97942
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 26280
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 600470
57.7%
Common 439516
42.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 45725
 
7.6%
I 43917
 
7.3%
a 39364
 
6.6%
T 37494
 
6.2%
C 35781
 
6.0%
L 35395
 
5.9%
Y 35047
 
5.8%
E 29674
 
4.9%
N 28506
 
4.7%
o 27846
 
4.6%
Other values (42) 241721
40.3%
Common
ValueCountFrequency (%)
' 162446
37.0%
97942
22.3%
: 43839
 
10.0%
, 35813
 
8.1%
_ 26280
 
6.0%
2 11355
 
2.6%
{ 8760
 
2.0%
} 8760
 
2.0%
8 6495
 
1.5%
1 5599
 
1.3%
Other values (14) 32227
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1039986
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 162446
 
15.6%
97942
 
9.4%
A 45725
 
4.4%
I 43917
 
4.2%
: 43839
 
4.2%
a 39364
 
3.8%
T 37494
 
3.6%
, 35813
 
3.4%
C 35781
 
3.4%
L 35395
 
3.4%
Other values (66) 462270
44.4%

BALCONY_NUM
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
1.0
4792 
0.0
2450 
2.0
1223 
3.0
 
228
4.0
 
67

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26280
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 4792
54.7%
0.0 2450
28.0%
2.0 1223
 
14.0%
3.0 228
 
2.6%
4.0 67
 
0.8%

Length

2025-07-24T18:41:31.209814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:31.282484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4792
54.7%
0.0 2450
28.0%
2.0 1223
 
14.0%
3.0 228
 
2.6%
4.0 67
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 11210
42.7%
. 8760
33.3%
1 4792
18.2%
2 1223
 
4.7%
3 228
 
0.9%
4 67
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17520
66.7%
Other Punctuation 8760
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11210
64.0%
1 4792
27.4%
2 1223
 
7.0%
3 228
 
1.3%
4 67
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 8760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26280
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11210
42.7%
. 8760
33.3%
1 4792
18.2%
2 1223
 
4.7%
3 228
 
0.9%
4 67
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11210
42.7%
. 8760
33.3%
1 4792
18.2%
2 1223
 
4.7%
3 228
 
0.9%
4 67
 
0.3%

FLOOR_NUM
Real number (ℝ)

High correlation  Zeros 

Distinct39
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7670091
Minimum-2
Maximum40
Zeros1209
Zeros (%)13.8%
Negative3
Negative (%)< 0.1%
Memory size68.6 KiB
2025-07-24T18:41:31.388826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q11
median2
Q34
95-th percentile14
Maximum40
Range42
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.8611203
Coefficient of variation (CV)1.2904456
Kurtosis8.0097633
Mean3.7670091
Median Absolute Deviation (MAD)1
Skewness2.5304147
Sum32999
Variance23.63049
MonotonicityNot monotonic
2025-07-24T18:41:31.507760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 2505
28.6%
2 1225
14.0%
0 1209
13.8%
3 1065
12.2%
4 721
 
8.2%
5 293
 
3.3%
10 213
 
2.4%
7 202
 
2.3%
8 202
 
2.3%
6 180
 
2.1%
Other values (29) 945
 
10.8%
ValueCountFrequency (%)
-2 2
 
< 0.1%
-1 1
 
< 0.1%
0 1209
13.8%
1 2505
28.6%
2 1225
14.0%
3 1065
12.2%
4 721
 
8.2%
5 293
 
3.3%
6 180
 
2.1%
7 202
 
2.3%
ValueCountFrequency (%)
40 1
 
< 0.1%
38 3
 
< 0.1%
36 1
 
< 0.1%
34 7
0.1%
33 3
 
< 0.1%
32 3
 
< 0.1%
31 2
 
< 0.1%
30 13
0.1%
28 7
0.1%
27 6
0.1%

CARPET_SQFT
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct742
Distinct (%)29.9%
Missing6278
Missing (%)71.7%
Infinite0
Infinite (%)0.0%
Mean3733.2555
Minimum0
Maximum4356000.2
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:31.662468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile400.75
Q1700
median900
Q31200
95-th percentile2000
Maximum4356000.2
Range4356000.2
Interquartile range (IQR)500

Descriptive statistics

Standard deviation94069.615
Coefficient of variation (CV)25.197744
Kurtosis1877.3662
Mean3733.2555
Median Absolute Deviation (MAD)247.5
Skewness41.897935
Sum9265940.2
Variance8.8490925 × 109
MonotonicityNot monotonic
2025-07-24T18:41:31.814268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
900 66
 
0.8%
700 61
 
0.7%
1000 60
 
0.7%
600 54
 
0.6%
1200 52
 
0.6%
800 50
 
0.6%
750 47
 
0.5%
1100 46
 
0.5%
650 42
 
0.5%
950 38
 
0.4%
Other values (732) 1966
 
22.4%
(Missing) 6278
71.7%
ValueCountFrequency (%)
0 11
0.1%
1.5 1
 
< 0.1%
7.5 1
 
< 0.1%
75 1
 
< 0.1%
100 2
 
< 0.1%
120 2
 
< 0.1%
130 3
 
< 0.1%
132 1
 
< 0.1%
140 2
 
< 0.1%
150 4
 
< 0.1%
ValueCountFrequency (%)
4356000.216 1
< 0.1%
1504799.988 1
< 0.1%
863999.9931 1
< 0.1%
8250 1
< 0.1%
6210 1
< 0.1%
6000 2
< 0.1%
5619 2
< 0.1%
5500 1
< 0.1%
5350 1
< 0.1%
4999 1
< 0.1%

SUPERBUILTUP_SQFT
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct1319
Distinct (%)24.5%
Missing3379
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean2323.7817
Minimum0
Maximum2994000
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:31.946228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile625
Q1890
median1140
Q31476
95-th percentile2490
Maximum2994000
Range2994000
Interquartile range (IQR)586

Descriptive statistics

Standard deviation53863.76
Coefficient of variation (CV)23.179354
Kurtosis2745.5508
Mean2323.7817
Median Absolute Deviation (MAD)284
Skewness52.265552
Sum12504270
Variance2.9013046 × 109
MonotonicityNot monotonic
2025-07-24T18:41:32.084005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1250 92
 
1.1%
1000 88
 
1.0%
850 84
 
1.0%
900 78
 
0.9%
1100 71
 
0.8%
800 68
 
0.8%
750 57
 
0.7%
1200 56
 
0.6%
1150 53
 
0.6%
1450 46
 
0.5%
Other values (1309) 4688
53.5%
(Missing) 3379
38.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
130 1
 
< 0.1%
150 1
 
< 0.1%
255 1
 
< 0.1%
280 1
 
< 0.1%
300 3
< 0.1%
310 1
 
< 0.1%
345 1
 
< 0.1%
350 1
 
< 0.1%
360 2
< 0.1%
ValueCountFrequency (%)
2994000 1
< 0.1%
2581176 1
< 0.1%
12956 1
< 0.1%
11000 1
< 0.1%
9238 1
< 0.1%
8500 1
< 0.1%
8000 1
< 0.1%
7780 1
< 0.1%
7143 1
< 0.1%
7014 1
< 0.1%

BUILTUP_SQFT
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct674
Distinct (%)40.3%
Missing7086
Missing (%)80.9%
Infinite0
Infinite (%)0.0%
Mean3094.5194
Minimum0
Maximum2160000
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:32.220480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile483.9
Q1750
median1000
Q31366.5
95-th percentile2445.7
Maximum2160000
Range2160000
Interquartile range (IQR)616.5

Descriptive statistics

Standard deviation58977.349
Coefficient of variation (CV)19.058645
Kurtosis1136.7921
Mean3094.5194
Median Absolute Deviation (MAD)292
Skewness32.915871
Sum5180225.6
Variance3.4783277 × 109
MonotonicityNot monotonic
2025-07-24T18:41:32.358895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700 38
 
0.4%
1000 33
 
0.4%
1100 29
 
0.3%
900 26
 
0.3%
800 25
 
0.3%
754 23
 
0.3%
1500 20
 
0.2%
750 19
 
0.2%
1600 17
 
0.2%
1400 17
 
0.2%
Other values (664) 1427
 
16.3%
(Missing) 7086
80.9%
ValueCountFrequency (%)
0 13
0.1%
100 2
 
< 0.1%
120 2
 
< 0.1%
144 1
 
< 0.1%
150 3
 
< 0.1%
160 2
 
< 0.1%
180 2
 
< 0.1%
200 1
 
< 0.1%
204 1
 
< 0.1%
250 3
 
< 0.1%
ValueCountFrequency (%)
2159999.983 1
< 0.1%
1079999.991 1
< 0.1%
11000 1
< 0.1%
9500 1
< 0.1%
8000 1
< 0.1%
7624 1
< 0.1%
7293 1
< 0.1%
6800 1
< 0.1%
6694 1
< 0.1%
6025 1
< 0.1%

SUPER_AREA
Real number (ℝ)

Missing  Skewed 

Distinct189
Distinct (%)23.2%
Missing7944
Missing (%)90.7%
Infinite0
Infinite (%)0.0%
Mean2502.6993
Minimum1
Maximum900000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:32.493406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.24
Q1160
median1080
Q31800
95-th percentile3600
Maximum900000
Range899999
Interquartile range (IQR)1640

Descriptive statistics

Standard deviation31558.603
Coefficient of variation (CV)12.609826
Kurtosis805.53433
Mean2502.6993
Median Absolute Deviation (MAD)810
Skewness28.296208
Sum2042202.6
Variance9.959454 × 108
MonotonicityNot monotonic
2025-07-24T18:41:32.635669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1440 69
 
0.8%
2160 49
 
0.6%
720 39
 
0.4%
3 38
 
0.4%
1800 36
 
0.4%
2 23
 
0.3%
1200 23
 
0.3%
1500 22
 
0.3%
300 19
 
0.2%
2880 18
 
0.2%
Other values (179) 480
 
5.5%
(Missing) 7944
90.7%
ValueCountFrequency (%)
1 4
 
< 0.1%
1.01 2
 
< 0.1%
1.5 3
 
< 0.1%
2 23
0.3%
2.01 1
 
< 0.1%
2.24 14
0.2%
2.25 13
0.1%
2.4 1
 
< 0.1%
2.5 9
 
0.1%
2.75 1
 
< 0.1%
ValueCountFrequency (%)
900000 1
< 0.1%
45360 1
< 0.1%
36000 1
< 0.1%
20000 1
< 0.1%
16000 1
< 0.1%
12000 2
< 0.1%
10800 1
< 0.1%
10080 1
< 0.1%
10000 1
< 0.1%
8750 1
< 0.1%

SUPERAREA_UNIT
Categorical

High correlation  Imbalance  Missing 

Distinct6
Distinct (%)0.7%
Missing7944
Missing (%)90.7%
Memory size546.8 KiB
sq.ft.
629 
kottah
162 
sq.m.
 
19
sq.yards
 
3
bigha
 
2

Length

Max length8
Median length6
Mean length5.9828431
Min length5

Characters and Unicode

Total characters4882
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowsq.ft.
2nd rowsq.ft.
3rd rowsq.ft.
4th rowkottah
5th rowsq.ft.

Common Values

ValueCountFrequency (%)
sq.ft. 629
 
7.2%
kottah 162
 
1.8%
sq.m. 19
 
0.2%
sq.yards 3
 
< 0.1%
bigha 2
 
< 0.1%
chataks 1
 
< 0.1%
(Missing) 7944
90.7%

Length

2025-07-24T18:41:32.767233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:32.852960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sq.ft 629
77.1%
kottah 162
 
19.9%
sq.m 19
 
2.3%
sq.yards 3
 
0.4%
bigha 2
 
0.2%
chataks 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 1299
26.6%
t 954
19.5%
s 655
13.4%
q 651
13.3%
f 629
12.9%
a 169
 
3.5%
h 165
 
3.4%
k 163
 
3.3%
o 162
 
3.3%
m 19
 
0.4%
Other values (7) 16
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3583
73.4%
Other Punctuation 1299
 
26.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 954
26.6%
s 655
18.3%
q 651
18.2%
f 629
17.6%
a 169
 
4.7%
h 165
 
4.6%
k 163
 
4.5%
o 162
 
4.5%
m 19
 
0.5%
y 3
 
0.1%
Other values (6) 13
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 1299
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3583
73.4%
Common 1299
 
26.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 954
26.6%
s 655
18.3%
q 651
18.2%
f 629
17.6%
a 169
 
4.7%
h 165
 
4.6%
k 163
 
4.5%
o 162
 
4.5%
m 19
 
0.5%
y 3
 
0.1%
Other values (6) 13
 
0.4%
Common
ValueCountFrequency (%)
. 1299
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1299
26.6%
t 954
19.5%
s 655
13.4%
q 651
13.3%
f 629
12.9%
a 169
 
3.5%
h 165
 
3.4%
k 163
 
3.3%
o 162
 
3.3%
m 19
 
0.4%
Other values (7) 16
 
0.3%

city
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size596.1 KiB
kolkata south
3784 
kolkata east
2872 
kolkata north
1789 
kolkata west
 
240
kolkata central
 
75

Length

Max length15
Median length13
Mean length12.661872
Min length12

Characters and Unicode

Total characters110918
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkolkata south
2nd rowkolkata south
3rd rowkolkata south
4th rowkolkata south
5th rowkolkata south

Common Values

ValueCountFrequency (%)
kolkata south 3784
43.2%
kolkata east 2872
32.8%
kolkata north 1789
20.4%
kolkata west 240
 
2.7%
kolkata central 75
 
0.9%

Length

2025-07-24T18:41:32.959775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:33.058148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
kolkata 8760
50.0%
south 3784
21.6%
east 2872
 
16.4%
north 1789
 
10.2%
west 240
 
1.4%
central 75
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 20467
18.5%
k 17520
15.8%
t 17520
15.8%
o 14333
12.9%
l 8835
8.0%
8760
7.9%
s 6896
 
6.2%
h 5573
 
5.0%
u 3784
 
3.4%
e 3187
 
2.9%
Other values (4) 4043
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 102158
92.1%
Space Separator 8760
 
7.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 20467
20.0%
k 17520
17.1%
t 17520
17.1%
o 14333
14.0%
l 8835
8.6%
s 6896
 
6.8%
h 5573
 
5.5%
u 3784
 
3.7%
e 3187
 
3.1%
n 1864
 
1.8%
Other values (3) 2179
 
2.1%
Space Separator
ValueCountFrequency (%)
8760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102158
92.1%
Common 8760
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 20467
20.0%
k 17520
17.1%
t 17520
17.1%
o 14333
14.0%
l 8835
8.6%
s 6896
 
6.8%
h 5573
 
5.5%
u 3784
 
3.7%
e 3187
 
3.1%
n 1864
 
1.8%
Other values (3) 2179
 
2.1%
Common
ValueCountFrequency (%)
8760
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 20467
18.5%
k 17520
15.8%
t 17520
15.8%
o 14333
12.9%
l 8835
8.0%
8760
7.9%
s 6896
 
6.2%
h 5573
 
5.0%
u 3784
 
3.4%
e 3187
 
2.9%
Other values (4) 4043
 
3.6%

side
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size496.3 KiB
3
3784 
1
2872 
2
1789 
4
 
240
0
 
75

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8760
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 3784
43.2%
1 2872
32.8%
2 1789
20.4%
4 240
 
2.7%
0 75
 
0.9%

Length

2025-07-24T18:41:33.169092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:33.242208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 3784
43.2%
1 2872
32.8%
2 1789
20.4%
4 240
 
2.7%
0 75
 
0.9%

Most occurring characters

ValueCountFrequency (%)
3 3784
43.2%
1 2872
32.8%
2 1789
20.4%
4 240
 
2.7%
0 75
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8760
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3784
43.2%
1 2872
32.8%
2 1789
20.4%
4 240
 
2.7%
0 75
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 8760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 3784
43.2%
1 2872
32.8%
2 1789
20.4%
4 240
 
2.7%
0 75
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 3784
43.2%
1 2872
32.8%
2 1789
20.4%
4 240
 
2.7%
0 75
 
0.9%
Distinct2820
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Memory size928.5 KiB
2025-07-24T18:41:33.545921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length79
Median length68
Mean length51.52089
Min length39

Characters and Unicode

Total characters451323
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1826 ?
Unique (%)20.8%

Sample

1st row{'LATITUDE': '22.364447', 'LONGITUDE': '88.274642'}
2nd row{'LATITUDE': '22.518795', 'LONGITUDE': '88.388439'}
3rd row{'LATITUDE': '22.5137646', 'LONGITUDE': '88.3666797'}
4th row{'LATITUDE': '22.45383', 'LONGITUDE': '88.249572'}
5th row{'LATITUDE': '22.44213', 'LONGITUDE': '88.29551'}
ValueCountFrequency (%)
latitude 8760
25.0%
longitude 8760
25.0%
22.6006912 418
 
1.2%
88.4694535 418
 
1.2%
88.35469 160
 
0.5%
22.536025 160
 
0.5%
88.2978736068 148
 
0.4%
22.4439029807 148
 
0.4%
22.5 130
 
0.4%
88.35 129
 
0.4%
Other values (5550) 15809
45.1%
2025-07-24T18:41:34.027755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 70080
 
15.5%
2 26969
 
6.0%
8 26712
 
5.9%
26280
 
5.8%
T 26280
 
5.8%
E 17520
 
3.9%
L 17520
 
3.9%
: 17520
 
3.9%
D 17520
 
3.9%
U 17520
 
3.9%
Other values (17) 187402
41.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 148920
33.0%
Decimal Number 144723
32.1%
Other Punctuation 113880
25.2%
Space Separator 26280
 
5.8%
Open Punctuation 8760
 
1.9%
Close Punctuation 8760
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 26969
18.6%
8 26712
18.5%
4 15299
10.6%
5 12967
9.0%
3 12852
8.9%
6 12584
8.7%
9 10117
 
7.0%
1 9131
 
6.3%
7 9090
 
6.3%
0 9002
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
T 26280
17.6%
E 17520
11.8%
L 17520
11.8%
D 17520
11.8%
U 17520
11.8%
I 17520
11.8%
A 8760
 
5.9%
O 8760
 
5.9%
G 8760
 
5.9%
N 8760
 
5.9%
Other Punctuation
ValueCountFrequency (%)
' 70080
61.5%
: 17520
 
15.4%
. 17520
 
15.4%
, 8760
 
7.7%
Space Separator
ValueCountFrequency (%)
26280
100.0%
Open Punctuation
ValueCountFrequency (%)
{ 8760
100.0%
Close Punctuation
ValueCountFrequency (%)
} 8760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 302403
67.0%
Latin 148920
33.0%

Most frequent character per script

Common
ValueCountFrequency (%)
' 70080
23.2%
2 26969
 
8.9%
8 26712
 
8.8%
26280
 
8.7%
: 17520
 
5.8%
. 17520
 
5.8%
4 15299
 
5.1%
5 12967
 
4.3%
3 12852
 
4.2%
6 12584
 
4.2%
Other values (7) 63620
21.0%
Latin
ValueCountFrequency (%)
T 26280
17.6%
E 17520
11.8%
L 17520
11.8%
D 17520
11.8%
U 17520
11.8%
I 17520
11.8%
A 8760
 
5.9%
O 8760
 
5.9%
G 8760
 
5.9%
N 8760
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 451323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 70080
 
15.5%
2 26969
 
6.0%
8 26712
 
5.9%
26280
 
5.8%
T 26280
 
5.8%
E 17520
 
3.9%
L 17520
 
3.9%
: 17520
 
3.9%
D 17520
 
3.9%
U 17520
 
3.9%
Other values (17) 187402
41.5%

latitude
Real number (ℝ)

Distinct2796
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.731371
Minimum0
Maximum88.491741
Zeros19
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:34.163129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.43993
Q122.493013
median22.568326
Q322.617476
95-th percentile22.72207
Maximum88.491741
Range88.491741
Interquartile range (IQR)0.1244628

Descriptive statistics

Standard deviation8.9639073
Coefficient of variation (CV)0.37772396
Kurtosis47.440935
Mean23.731371
Median Absolute Deviation (MAD)0.062158
Skewness6.9347323
Sum207886.81
Variance80.351633
MonotonicityNot monotonic
2025-07-24T18:41:35.084870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.6006912 420
 
4.8%
22.44390298 148
 
1.7%
22.5 130
 
1.5%
88.35469 102
 
1.2%
22.548114 84
 
1.0%
22.581047 76
 
0.9%
22.569 74
 
0.8%
22.4658051 64
 
0.7%
22.536025 59
 
0.7%
22.60134 57
 
0.7%
Other values (2786) 7546
86.1%
ValueCountFrequency (%)
0 19
0.2%
13.036358 1
 
< 0.1%
13.06146 1
 
< 0.1%
20.60288 9
0.1%
21.623056 2
 
< 0.1%
22.28355 1
 
< 0.1%
22.3269857 1
 
< 0.1%
22.350618 1
 
< 0.1%
22.361331 2
 
< 0.1%
22.361462 2
 
< 0.1%
ValueCountFrequency (%)
88.491741 1
 
< 0.1%
88.465164 1
 
< 0.1%
88.4616753 1
 
< 0.1%
88.450763 13
 
0.1%
88.439278 1
 
< 0.1%
88.4054689 1
 
< 0.1%
88.378133 29
 
0.3%
88.36087 4
 
< 0.1%
88.35469 102
1.2%
88.330442 1
 
< 0.1%

longitude
Real number (ℝ)

Distinct2766
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.986636
Minimum0
Maximum91.820293
Zeros19
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:35.215011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile88.293918
Q188.360697
median88.401162
Q388.45989
95-th percentile88.502793
Maximum91.820293
Range91.820293
Interquartile range (IQR)0.099193

Descriptive statistics

Standard deviation9.7776437
Coefficient of variation (CV)0.11240398
Kurtosis45.659213
Mean86.986636
Median Absolute Deviation (MAD)0.0511222
Skewness-6.8451065
Sum762002.93
Variance95.602316
MonotonicityNot monotonic
2025-07-24T18:41:35.348750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.4694535 420
 
4.8%
88.29787361 148
 
1.7%
88.35 129
 
1.5%
22.536025 102
 
1.2%
88.4004968 84
 
1.0%
88.462231 76
 
0.9%
88.510296 74
 
0.8%
88.384625 74
 
0.8%
88.35469 59
 
0.7%
88.471506 57
 
0.7%
Other values (2756) 7537
86.0%
ValueCountFrequency (%)
0 19
 
0.2%
22.465619 1
 
< 0.1%
22.48357 1
 
< 0.1%
22.493402 2
 
< 0.1%
22.5102343 1
 
< 0.1%
22.5103 1
 
< 0.1%
22.511184 1
 
< 0.1%
22.536025 102
1.2%
22.58802 4
 
< 0.1%
22.59436 4
 
< 0.1%
ValueCountFrequency (%)
91.820293 1
 
< 0.1%
88.609395 1
 
< 0.1%
88.538014 1
 
< 0.1%
88.53401 2
 
< 0.1%
88.53073024 3
 
< 0.1%
88.5255283 6
 
0.1%
88.525052 3
 
< 0.1%
88.524436 1
 
< 0.1%
88.523625 5
 
0.1%
88.522544 45
0.5%

society
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.1%
Missing1370
Missing (%)15.6%
Memory size518.8 KiB
1.0
2608 
2.0
1732 
3.0
1324 
4.0
1320 
5.0
406 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22170
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 2608
29.8%
2.0 1732
19.8%
3.0 1324
15.1%
4.0 1320
15.1%
5.0 406
 
4.6%
(Missing) 1370
15.6%

Length

2025-07-24T18:41:35.487837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:35.580461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2608
35.3%
2.0 1732
23.4%
3.0 1324
17.9%
4.0 1320
17.9%
5.0 406
 
5.5%

Most occurring characters

ValueCountFrequency (%)
. 7390
33.3%
0 7390
33.3%
1 2608
 
11.8%
2 1732
 
7.8%
3 1324
 
6.0%
4 1320
 
6.0%
5 406
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14780
66.7%
Other Punctuation 7390
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7390
50.0%
1 2608
 
17.6%
2 1732
 
11.7%
3 1324
 
9.0%
4 1320
 
8.9%
5 406
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 7390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22170
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 7390
33.3%
0 7390
33.3%
1 2608
 
11.8%
2 1732
 
7.8%
3 1324
 
6.0%
4 1320
 
6.0%
5 406
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 7390
33.3%
0 7390
33.3%
1 2608
 
11.8%
2 1732
 
7.8%
3 1324
 
6.0%
4 1320
 
6.0%
5 406
 
1.8%

preference
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size496.3 KiB
2
7026 
1
1352 
0
 
382

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8760
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 7026
80.2%
1 1352
 
15.4%
0 382
 
4.4%

Length

2025-07-24T18:41:35.677834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:35.747453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 7026
80.2%
1 1352
 
15.4%
0 382
 
4.4%

Most occurring characters

ValueCountFrequency (%)
2 7026
80.2%
1 1352
 
15.4%
0 382
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8760
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 7026
80.2%
1 1352
 
15.4%
0 382
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 8760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 7026
80.2%
1 1352
 
15.4%
0 382
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 7026
80.2%
1 1352
 
15.4%
0 382
 
4.4%

type
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size496.3 KiB
2
7518 
0
 
497
3
 
476
1
 
269

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8760
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 7518
85.8%
0 497
 
5.7%
3 476
 
5.4%
1 269
 
3.1%

Length

2025-07-24T18:41:35.832105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-24T18:41:35.912781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 7518
85.8%
0 497
 
5.7%
3 476
 
5.4%
1 269
 
3.1%

Most occurring characters

ValueCountFrequency (%)
2 7518
85.8%
0 497
 
5.7%
3 476
 
5.4%
1 269
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8760
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 7518
85.8%
0 497
 
5.7%
3 476
 
5.4%
1 269
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 7518
85.8%
0 497
 
5.7%
3 476
 
5.4%
1 269
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 7518
85.8%
0 497
 
5.7%
3 476
 
5.4%
1 269
 
3.1%

AmenityScore
Real number (ℝ)

High correlation  Zeros 

Distinct1250
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.44145
Minimum0
Maximum161.2
Zeros2231
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:36.013493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31.75
Q364.05
95-th percentile122.6
Maximum161.2
Range161.2
Interquartile range (IQR)64.05

Descriptive statistics

Standard deviation41.089362
Coefficient of variation (CV)0.96814227
Kurtosis-0.3696457
Mean42.44145
Median Absolute Deviation (MAD)31.75
Skewness0.80773987
Sum371787.1
Variance1688.3356
MonotonicityNot monotonic
2025-07-24T18:41:36.145303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2231
25.5%
61.5 427
 
4.9%
23.5 142
 
1.6%
9.5 122
 
1.4%
7.4 118
 
1.3%
7.8 113
 
1.3%
28.4 99
 
1.1%
29.8 67
 
0.8%
37.6 63
 
0.7%
24.7 62
 
0.7%
Other values (1240) 5316
60.7%
ValueCountFrequency (%)
0 2231
25.5%
3.5 13
 
0.1%
4 6
 
0.1%
4.2 12
 
0.1%
5.9 60
 
0.7%
6.3 1
 
< 0.1%
7 17
 
0.2%
7.1 1
 
< 0.1%
7.3 7
 
0.1%
7.4 118
 
1.3%
ValueCountFrequency (%)
161.2 2
 
< 0.1%
156.5 2
 
< 0.1%
153.4 3
 
< 0.1%
153.3 1
 
< 0.1%
152 44
0.5%
150.2 1
 
< 0.1%
148.6 1
 
< 0.1%
148.2 1
 
< 0.1%
148 29
0.3%
147.8 1
 
< 0.1%

Price
Real number (ℝ)

High correlation 

Distinct2166
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1669192
Minimum0.00015
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2025-07-24T18:41:36.273097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.00015
5-th percentile0.00125
Q10.182375
median0.46
Q30.8582
95-th percentile2.96
Maximum70
Range69.99985
Interquartile range (IQR)0.675825

Descriptive statistics

Standard deviation4.3881424
Coefficient of variation (CV)3.7604509
Kurtosis92.455851
Mean1.1669192
Median Absolute Deviation (MAD)0.34
Skewness9.3381857
Sum10222.212
Variance19.255793
MonotonicityNot monotonic
2025-07-24T18:41:36.425983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.65 110
 
1.3%
0.75 106
 
1.2%
0.0025 90
 
1.0%
0.85 87
 
1.0%
0.45 83
 
0.9%
0.55 77
 
0.9%
0.8 73
 
0.8%
0.7 71
 
0.8%
0.002 71
 
0.8%
0.42 67
 
0.8%
Other values (2156) 7925
90.5%
ValueCountFrequency (%)
0.00015 3
< 0.1%
0.0002 3
< 0.1%
0.00022 3
< 0.1%
0.0002201 1
 
< 0.1%
0.000245 1
 
< 0.1%
0.00025 4
< 0.1%
0.00026 1
 
< 0.1%
0.00027 3
< 0.1%
0.00028 3
< 0.1%
0.00029 1
 
< 0.1%
ValueCountFrequency (%)
70 1
 
< 0.1%
60 1
 
< 0.1%
50.63 2
< 0.1%
50.485 1
 
< 0.1%
49.92 3
< 0.1%
49.56 2
< 0.1%
49.165 2
< 0.1%
48.86 4
< 0.1%
48.84 1
 
< 0.1%
48.15 1
 
< 0.1%

Interactions

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2025-07-24T18:40:57.412565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:59.451958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:01.109202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:03.425066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:05.326864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:07.495407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:09.065910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:10.644729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:12.276170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:14.092357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:17.438648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:45.731837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:49.851793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:52.320463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:54.092185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:55.857140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:57.503305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:59.562617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:01.202821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:03.609849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:05.431560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:07.590524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:09.166311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:10.735053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:12.366315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:14.248774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:17.540209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:45.935051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:50.002139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:52.423333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:54.197498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:55.953022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:57.595958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:59.681230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:01.367875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:03.772516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:05.536454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:07.702855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:09.254274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:10.839946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:12.465816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:14.422458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:17.653996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:46.206322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:50.165143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:52.529565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:54.320394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:56.073583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:57.700052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:40:59.788215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:01.532325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:03.940142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:05.677480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:07.810214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:09.354179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:10.947991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:12.566758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-24T18:41:14.596488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-24T18:41:36.583748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AGEAREAAmenityScoreBALCONY_NUMBEDROOM_NUMBUILTUP_SQFTCARPET_SQFTCITYFACINGFLOOR_NUMFURNISHOWNTYPEPREFERENCEPRICE_SQFTPROPERTY_TYPEPriceSUPERAREA_UNITSUPERBUILTUP_SQFTSUPER_AREATOTAL_FLOORTOTAL_LANDMARK_COUNTTRANSACT_TYPEcitylatitudelongitudepreferencesidesocietytype
AGE1.000-0.041-0.1950.3350.1020.0780.0560.132-0.267-0.0230.4430.1850.3040.2210.5950.2330.0970.079-0.0660.223-0.1660.5100.1320.035-0.0590.3040.1320.1640.595
AREA-0.0411.0000.1370.0000.7910.9320.9020.000-0.0210.1120.0130.0000.0270.3160.0510.5240.3460.9990.4180.285-0.0370.0000.000-0.0530.0770.0270.0000.0000.051
AmenityScore-0.1950.1371.0000.3530.0640.1300.1640.0970.5010.6400.3910.0880.197-0.0390.182-0.0140.0630.2660.0550.2270.0770.4700.0970.0620.1030.1970.0970.1650.182
BALCONY_NUM0.3350.0000.3531.0000.2000.0000.0000.0850.3000.2650.4900.1370.1650.0360.2430.0930.1200.0000.0000.1240.2130.5480.0850.0410.0410.1650.0850.1150.243
BEDROOM_NUM0.1020.7910.0640.2001.0000.7950.7450.000-0.0700.0840.0460.0360.0640.3470.2400.5330.1710.8260.2550.249-0.0880.0210.000-0.0260.0300.0640.0000.0170.240
BUILTUP_SQFT0.0780.9320.1300.0000.7951.0000.9800.0000.0130.2360.0250.0000.0940.2970.0750.5020.7930.9560.4700.3060.0350.0260.000-0.0540.0180.0940.0001.0000.075
CARPET_SQFT0.0560.9020.1640.0000.7450.9801.0000.000-0.0250.2320.0130.0000.0600.2190.0950.4210.4800.9390.2410.3390.0190.0000.000-0.0140.1310.0600.0000.0000.095
CITY0.1320.0000.0970.0850.0000.0000.0001.0000.0900.0770.0640.0690.1130.0310.0970.0680.1630.0000.0000.1580.2730.0891.0000.0430.0570.1131.0000.1480.097
FACING-0.267-0.0210.5010.300-0.0700.013-0.0250.0901.0000.3150.3600.0690.125-0.1670.086-0.1560.014-0.0420.031-0.1430.2290.5170.0900.017-0.0180.1250.0900.0910.086
FLOOR_NUM-0.0230.1120.6400.2650.0840.2360.2320.0770.3151.0000.2740.0890.1830.0490.1850.0711.0000.367NaN0.4700.0290.3070.0770.0710.1230.1830.0770.1840.185
FURNISH0.4430.0130.3910.4900.0460.0250.0130.0640.3600.2741.0000.3250.4110.0330.2560.0900.1310.0000.0000.1340.2780.6600.0640.0530.0520.4110.0640.1370.256
OWNTYPE0.1850.0000.0880.1370.0360.0000.0000.0690.0690.0890.3251.0000.7070.0140.1280.0310.2350.0000.0000.0560.1260.7090.0690.0160.0170.7070.0690.0950.128
PREFERENCE0.3040.0270.1970.1650.0640.0940.0600.1130.1250.1830.4110.7071.0000.0050.3030.0290.1680.0000.0000.1500.1890.7070.1130.0240.0241.0000.1130.1670.303
PRICE_SQFT0.2210.316-0.0390.0360.3470.2970.2190.031-0.1670.0490.0330.0140.0051.0000.1030.9160.2070.374-0.3120.352-0.1370.0210.031-0.0590.0330.0050.0310.0000.103
PROPERTY_TYPE0.5950.0510.1820.2430.2400.0750.0950.0970.0860.1850.2560.1280.3030.1031.0000.0530.1750.0000.0000.1860.1660.1850.0970.1110.1110.3030.0970.1141.000
Price0.2330.524-0.0140.0930.5330.5020.4210.068-0.1560.0710.0900.0310.0290.9160.0531.0000.3150.5530.0420.349-0.1340.1440.068-0.0350.0610.0290.0680.0460.053
SUPERAREA_UNIT0.0970.3460.0630.1200.1710.7930.4800.1630.0141.0000.1310.2350.1680.2070.1750.3151.000NaN0.0000.2270.1460.1660.1630.0800.0800.1680.1630.1360.175
SUPERBUILTUP_SQFT0.0790.9990.2660.0000.8260.9560.9390.000-0.0420.3670.0000.0000.0000.3740.0000.553NaN1.000NaN0.492-0.0410.0000.000-0.0520.0860.0000.0000.0000.000
SUPER_AREA-0.0660.4180.0550.0000.2550.4700.2410.0000.031NaN0.0000.0000.000-0.3120.0000.0420.000NaN1.000-0.082-0.1990.0000.000-0.241-0.1510.0000.0000.0000.000
TOTAL_FLOOR0.2230.2850.2270.1240.2490.3060.3390.158-0.1430.4700.1340.0560.1500.3520.1860.3490.2270.492-0.0821.000-0.3890.2580.1580.0770.2210.1500.1580.3290.186
TOTAL_LANDMARK_COUNT-0.166-0.0370.0770.213-0.0880.0350.0190.2730.2290.0290.2780.1260.189-0.1370.166-0.1340.146-0.041-0.199-0.3891.0000.3670.273-0.062-0.1870.1890.2730.2630.166
TRANSACT_TYPE0.5100.0000.4700.5480.0210.0260.0000.0890.5170.3070.6600.7090.7070.0210.1850.1440.1660.0000.0000.2580.3671.0000.0890.0520.0530.7070.0890.1900.185
city0.1320.0000.0970.0850.0000.0000.0001.0000.0900.0770.0640.0690.1130.0310.0970.0680.1630.0000.0000.1580.2730.0891.0000.0430.0570.1131.0000.1480.097
latitude0.035-0.0530.0620.041-0.026-0.054-0.0140.0430.0170.0710.0530.0160.024-0.0590.111-0.0350.080-0.052-0.2410.077-0.0620.0520.0431.0000.4040.0240.0430.0480.111
longitude-0.0590.0770.1030.0410.0300.0180.1310.057-0.0180.1230.0520.0170.0240.0330.1110.0610.0800.086-0.1510.221-0.1870.0530.0570.4041.0000.0240.0570.0490.111
preference0.3040.0270.1970.1650.0640.0940.0600.1130.1250.1830.4110.7071.0000.0050.3030.0290.1680.0000.0000.1500.1890.7070.1130.0240.0241.0000.1130.1670.303
side0.1320.0000.0970.0850.0000.0000.0001.0000.0900.0770.0640.0690.1130.0310.0970.0680.1630.0000.0000.1580.2730.0891.0000.0430.0570.1131.0000.1480.097
society0.1640.0000.1650.1150.0171.0000.0000.1480.0910.1840.1370.0950.1670.0000.1140.0460.1360.0000.0000.3290.2630.1900.1480.0480.0490.1670.1481.0000.114
type0.5950.0510.1820.2430.2400.0750.0950.0970.0860.1850.2560.1280.3030.1031.0000.0530.1750.0000.0000.1860.1660.1850.0970.1110.1110.3030.0970.1141.000

Missing values

2025-07-24T18:41:17.908854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-24T18:41:18.269538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-24T18:41:18.606850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PROP_IDPREFERENCEDESCRIPTIONPROPERTY_TYPECITYTRANSACT_TYPEOWNTYPEBEDROOM_NUMFURNISHFACINGAGETOTAL_FLOORFEATURESPROP_NAMEPRICE_SQFTMAP_DETAILSAMENITIESAREAPROP_HEADINGSECONDARY_TAGSTOTAL_LANDMARK_COUNTFORMATTED_LANDMARK_DETAILSSOCIETY_NAMEBUILDING_NAMElocationBALCONY_NUMFLOOR_NUMCARPET_SQFTSUPERBUILTUP_SQFTBUILTUP_SQFTSUPER_AREASUPERAREA_UNITcitysidelat_longlatitudelongitudesocietypreferencetypeAmenityScorePrice
0Y71306776SBook your 2 BHK flat in Srijan Star Swapno Puron, Amtala , Kolkata South. Having a super built-up area of 518.0 sq. ft. - 623.0 sq. ft., these flats promise an exclusive view and refreshing vibes, making them well ventilated.\n\nIt is a superbly design project set amidst excellent surroundings and offers residents a world-class infrastructure. Look at its range of general amenities to premium amenities that include Car Parking, Cafeteria, 24/7 Power Backup, Senior Citizen Sitout, Carrom and what not. Now, you can buy this exclusive 2 BHK flat at a price range of Rs. 19.1 Lac - Rs. 22.67 Lac.Residential ApartmentKolkata South2.012.00053.00Srijan Star Swapno Puron3662.0{'LATITUDE': '22.364447', 'LONGITUDE': '88.274642'}0570.52 BHK Flat in Amtala['UNDER CONSTRUCTION', 'NEW BOOKING', 'RERA | HIRA']2.0[{'category': 'Shopping', 'text': '1 Shopping', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'Hospital', 'text': '1 Hospital', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}]Srijan Star Swapno PuronSrijan Star Swapno Puron{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '8005', 'LOCALITY_NAME': 'Amtala ', 'ADDRESS': None}0.01.0NaNNaNNaNNaNNaNkolkata south3{'LATITUDE': '22.364447', 'LONGITUDE': '88.274642'}22.36444788.2746422.0220.00.19100
1B70113976SMake Natural Quest your next home. This project offers 3 BHK flats in EM Bypass, Kolkata South. With a built-up area ranging from 1110.0 sq. ft. to 1121.0 sq. ft., these flats combine the finest design and amenities in Kolkata South to provide a living experience unlike any other.\n\nIt is a new launch project and is unique in its perfect harmony of classic form and modern construction. The features and the amenities like Car Parking, Paved Compound, CCTV Camera Security, Pergola, Reflexology Park and many more make this residential project an epitome of modern living. Further, the society is well connected with all means of public transport. The flats are available at a price range of Rs. 1.17 Crore to Rs. 1.18 Crore.Residential ApartmentKolkata South2.013.000511.00Natural Quest10500.0{'LATITUDE': '22.518795', 'LONGITUDE': '88.388439'}01115.53 BHK Flat in EM Bypass['UNDER CONSTRUCTION', 'NEW LAUNCH', 'NEW BOOKING', 'RERA | HIRA']9.0[{'category': 'MetroStation', 'text': '1 Metro Station', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'Shopping', 'text': '2 Shoppings', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'Education', 'text': '2 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'Hospital', 'text': '2 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Airport', 'text': '1 Airport', 'className': 'aprt', 'icon': 'https://static.99acres.com/universalapp/img/aprt.png'}, {'category': 'RailwayStation', 'text': '1 Railway Station', 'className': 'rail', 'icon': 'https://static.99acres.com/universalapp/img/rail.png'}]Natural QuestNatural Quest{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '603', 'LOCALITY_NAME': 'EM Bypass', 'ADDRESS': None}0.01.0NaNNaNNaNNaNNaNkolkata south3{'LATITUDE': '22.518795', 'LONGITUDE': '88.388439'}22.51879588.3884391.0220.01.17500
2O70374510SBook your 3 BHK apartment in Garia, Kolkata South at a price ranging from Rs. 1.22 Crore to Rs. 1.35 Crore. The Ganguly 4Sight Eminence hosts exclusively designed towers, each presenting an epitome of class and simplicity.\n\nThe residential apartments have a super built-up area of 1376.0 sq. ft. to 1516.0 sq. ft. and are a new launch. With an impressive layout and a comprehensive range of amenities, like , etc., the Ganguly 4Sight Eminence leaves no stone unturned to amaze.Residential ApartmentKolkata South2.013.000520.00Ganguly 4Sight Eminence8900.0{'LATITUDE': '22.5137646', 'LONGITUDE': '88.3666797'}01446.03 BHK Flat in Garia['UNDER CONSTRUCTION', 'NEW LAUNCH', 'NEW BOOKING', 'RERA | HIRA']6.0[{'category': 'Shopping', 'text': '1 Shopping', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'Education', 'text': '2 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'Hospital', 'text': '1 Hospital', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Airport', 'text': '1 Airport', 'className': 'aprt', 'icon': 'https://static.99acres.com/universalapp/img/aprt.png'}, {'category': 'RailwayStation', 'text': '1 Railway Station', 'className': 'rail', 'icon': 'https://static.99acres.com/universalapp/img/rail.png'}]Ganguly 4Sight EminenceGanguly 4Sight Eminence{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '651', 'LOCALITY_NAME': 'Garia', 'ADDRESS': None}0.01.0NaNNaNNaNNaNNaNkolkata south3{'LATITUDE': '22.5137646', 'LONGITUDE': '88.3666797'}22.51376588.3666801.0220.01.28500
3Q69170182SDev bhumi in joka, kolkata south by ocean land developer is a residential project. \n \nDev bhumi price ranges from 1.49 cr to 5.99 cr. \n \nIt also offers car parking. \n \nThe project is spread over a total area of 2 acres of land. It has 50% of open space. \nAn accommodation of 500 units has been provided. \n \n \nDev bhumi brochure is also available for easy reference. \n \nAbout city: \n \nKolkata, the city of joy has a realty market scenario like no other. The addition of emerging townships in the city has affected the residential real estate market hugely. Along with this, the developing infrastructure, presence of large industries, well connectivity between major micro markets and a stable economy of the state has helped kolkata create a positive feel throughout.Residential LandKolkata South2.01NaN0000.00Dev Bhumi208.0{'LATITUDE': '22.45383', 'LONGITUDE': '88.249572'}01800.0Residential land / Plot in Joka['READY TO MOVE', 'NEW BOOKING']13.0[{'category': 'MetroStation', 'text': '2 Metro Stations', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'Connectivity', 'text': '1 Connectivity', 'className': 'hw', 'icon': 'https://static.99acres.com/universalapp/img/hw.png'}, {'category': 'Education', 'text': '6 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'Hospital', 'text': '4 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}]Dev BhumiDev Bhumi{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '844', 'LOCALITY_NAME': 'Joka', 'ADDRESS': None}0.00.0NaNNaNNaNNaNNaNkolkata south3{'LATITUDE': '22.45383', 'LONGITUDE': '88.249572'}22.45383088.2495722.0230.00.03745
4F69917588SLet your dream of owning a flat come true with DTC Sojon. It offers an exclusive range of 3 BHK flats in Joka, Kolkata South. They are available at price of Rs. 60 Lac - Rs. 75 Lac and have a super built-up area of 1130.0 sq. ft. to 1460.0 sq. ft.\n\nThe project is a secured gated community that further has 24x7 security systems. It has round the clock power back up as well as features many more attractive facilities with a host of amenities like Senior Citizen Sitout, CCTV Camera Security, 24/7 Water Supply, Toddler Pool, Reflexology Park, etc.Residential ApartmentKolkata South2.013.000519.00DTC Sojon5215.0{'LATITUDE': '22.44213', 'LONGITUDE': '88.29551'}01295.03 BHK Flat in Joka['UNDER CONSTRUCTION', 'NEW BOOKING']11.0[{'category': 'MetroStation', 'text': '1 Metro Station', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'Shopping', 'text': '1 Shopping', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'Connectivity', 'text': '1 Connectivity', 'className': 'hw', 'icon': 'https://static.99acres.com/universalapp/img/hw.png'}, {'category': 'Education', 'text': '2 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'Hospital', 'text': '1 Hospital', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Airport', 'text': '1 Airport', 'className': 'aprt', 'icon': 'https://static.99acres.com/universalapp/img/aprt.png'}, {'category': 'RailwayStation', 'text': '1 Railway Station', 'className': 'rail', 'icon': 'https://static.99acres.com/universalapp/img/rail.png'}, {'category': 'Hotel', 'text': '1 Hotels', 'className': 'htl', 'icon': 'https://static.99acres.com/universalapp/img/htl.png'}, {'category': 'GolfCourse', 'text': '1 Golf Course', 'className': 'golf', 'icon': 'https://static.99acres.com/universalapp/img/golf.png'}, {'category': 'Stadium', 'text': '1 Stadium', 'className': 'stdm', 'icon': 'https://static.99acres.com/universalapp/img/stdm.png'}]DTC SojonDTC Sojon{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '844', 'LOCALITY_NAME': 'Joka', 'ADDRESS': None}0.01.0NaNNaNNaNNaNNaNkolkata south3{'LATITUDE': '22.44213', 'LONGITUDE': '88.29551'}22.44213088.2955103.0220.00.67500
5Y69917586SDTC Sojon offers 2 BHK apartments in Joka, Kolkata South, at an affordable price range of Rs. 45 Lac to Rs. 49 Lac.\n\nThese are an under construction apartments spreading across a super built-up area of 880.0 sq. ft. - 960.0 sq. ft. There are a total of 11 towers in the project. The project offers inclusion of essential amenities like Cricket Pitch, Carrom, Gated Community, Gymnasium, Senior Citizen Sitout, etc. Its aesthetics too is tastefully done to suit to your urbane living needs and promises to offer an easy living within budget.Residential ApartmentKolkata South2.012.000519.00DTC Sojon5108.0{'LATITUDE': '22.44213', 'LONGITUDE': '88.29551'}0920.02 BHK Flat in Joka['UNDER CONSTRUCTION', 'NEW BOOKING']11.0[{'category': 'MetroStation', 'text': '1 Metro Station', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'Shopping', 'text': '1 Shopping', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'Connectivity', 'text': '1 Connectivity', 'className': 'hw', 'icon': 'https://static.99acres.com/universalapp/img/hw.png'}, {'category': 'Education', 'text': '2 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'Hospital', 'text': '1 Hospital', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Airport', 'text': '1 Airport', 'className': 'aprt', 'icon': 'https://static.99acres.com/universalapp/img/aprt.png'}, {'category': 'RailwayStation', 'text': '1 Railway Station', 'className': 'rail', 'icon': 'https://static.99acres.com/universalapp/img/rail.png'}, {'category': 'Hotel', 'text': '1 Hotels', 'className': 'htl', 'icon': 'https://static.99acres.com/universalapp/img/htl.png'}, {'category': 'GolfCourse', 'text': '1 Golf Course', 'className': 'golf', 'icon': 'https://static.99acres.com/universalapp/img/golf.png'}, {'category': 'Stadium', 'text': '1 Stadium', 'className': 'stdm', 'icon': 'https://static.99acres.com/universalapp/img/stdm.png'}]DTC SojonDTC Sojon{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '844', 'LOCALITY_NAME': 'Joka', 'ADDRESS': None}0.01.0NaNNaNNaNNaNNaNkolkata south3{'LATITUDE': '22.44213', 'LONGITUDE': '88.29551'}22.44213088.2955103.0220.00.47000
6G68329262SMake The Signature your next home. This project offers 3 BHK flats in New Alipore, Kolkata South. With a super built-up area ranging from 1289.0 sq. ft. to 1422.0 sq. ft., these flats combine the finest design and amenities in Kolkata South to provide a living experience unlike any other.\n\nIt is a ready to move project and is unique in its perfect harmony of classic form and modern construction. The features and the amenities like Gymnasium, Gated Community, Children's Play Area, 24/7 Water Supply, Party Lawn and many more make this residential project an epitome of modern living. Further, the society is well connected with all means of public transport. The flats are available at a price range of Rs. 90.85 Lac to Rs. 1 Crore.Residential ApartmentKolkata South2.013.000511.00The Signature7048.0{'LATITUDE': '22.499927', 'LONGITUDE': '88.32487'}01355.53 BHK Flat in New Alipore['UNDER CONSTRUCTION', 'NEW BOOKING', 'RERA | HIRA']48.0[{'category': 'Shopping', 'text': '1 Shopping', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'ReligiousPlace', 'text': '6 Religious Places', 'className': 'rlgs', 'icon': 'https://static.99acres.com/universalapp/img/rlgs.png'}, {'category': 'ATM', 'text': '4 ATMs', 'className': 'atm', 'icon': 'https://static.99acres.com/universalapp/img/atm_blue.png'}, {'category': 'Hospital', 'text': '19 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Pharmacy', 'text': '2 Pharmacys', 'className': 'phmcy', 'icon': 'https://static.99acres.com/universalapp/img/phmcy.png'}, {'category': 'BusDepot', 'text': '1 Bus Depot', 'className': 'busdpt', 'icon': 'https://static.99acres.com/universalapp/img/busdpt.png'}]The SignatureThe Signature{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '1465', 'LOCALITY_NAME': 'New Alipore', 'ADDRESS': None}0.01.0NaNNaNNaNNaNNaNkolkata south3{'LATITUDE': '22.499927', 'LONGITUDE': '88.32487'}22.49992788.3248701.0220.00.90850
7J71214794SExperience a new style of living with Bhawani Bandhan. It offers an exclusive range of 2 BHK apartments in Madhyamgram, Kolkata North. Here is a steal deal for you. Book your 2 BHK apartment here at a never before price of Rs. 39 Lac. The unit has a super built-up area of 920.0 sq. ft. \n\nThis is an under construction project. It has been designed keeping every small to large needs of residents in consideration. Plus, a comprehensive range of amenities including Indoor Games, Card Room, Gymnasium, Lift(s), Banquet Hall, etc. make it one of the most desirable residential projects in Kolkata North.Residential ApartmentKolkata North2.012.000511.00Bhawani Bandhan4239.0{'LATITUDE': '22.690003', 'LONGITUDE': '88.45908'}0920.02 BHK Flat in Madhyamgram['UNDER CONSTRUCTION', 'NEW BOOKING', 'RERA | HIRA']27.0[{'category': 'ReligiousPlace', 'text': '5 Religious Places', 'className': 'rlgs', 'icon': 'https://static.99acres.com/universalapp/img/rlgs.png'}, {'category': 'Hospital', 'text': '15 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Attraction', 'text': '1 Attraction', 'className': 'cultrl', 'icon': 'https://static.99acres.com/universalapp/img/cultrl.png'}, {'category': 'BusDepot', 'text': '1 Bus Depot', 'className': 'busdpt', 'icon': 'https://static.99acres.com/universalapp/img/busdpt.png'}]Bhawani BandhanBhawani Bandhan{'CITY': '26', 'CITY_NAME': 'Kolkata North', 'LOCALITY_ID': '528', 'LOCALITY_NAME': 'Madhyamgram', 'ADDRESS': None}0.01.0NaN920.0NaNNaNNaNkolkata north2{'LATITUDE': '22.690003', 'LONGITUDE': '88.45908'}22.69000388.4590802.0220.00.39000
8K71064488SSymphony Proxima is a new launch project, offering 3 BHK flats in Kamalgazi, Kolkata South. These flats have a super built-up area in the range of 1120.0 sq. ft. to 1280.0 sq. ft. and are available at an economical price of Rs. 52.64 Lac - Rs. 60.16 Lac.\n\nIt is well connected to the city areas and features a large number of amenities to fit your needs. It is equipped with highlights such as Banquet Hall, Yoga/Meditation Area, Gymnasium, Children's Play Area, Senior Citizen Sitout, etc. that make it one of the most sought after neighborhoods.Residential ApartmentKolkata South2.013.00055.00Symphony Proxima4700.0{'LATITUDE': '22.44497560154', 'LONGITUDE': '88.390239965808'}01200.03 BHK Flat in Kamalgazi['UNDER CONSTRUCTION', 'NEW LAUNCH', 'NEW BOOKING', 'RERA | HIRA']7.0[{'category': 'MetroStation', 'text': '1 Metro Station', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'Shopping', 'text': '1 Shopping', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'Education', 'text': '2 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'Hospital', 'text': '1 Hospital', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Airport', 'text': '1 Airport', 'className': 'aprt', 'icon': 'https://static.99acres.com/universalapp/img/aprt.png'}, {'category': 'RailwayStation', 'text': '1 Railway Station', 'className': 'rail', 'icon': 'https://static.99acres.com/universalapp/img/rail.png'}]Symphony ProximaSymphony Proxima{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '6849', 'LOCALITY_NAME': 'Kamalgazi', 'ADDRESS': None}0.01.0NaNNaNNaNNaNNaNkolkata south3{'LATITUDE': '22.44497560154', 'LONGITUDE': '88.390239965808'}22.44497688.3902401.0220.00.56400
9Q71064486SThis well-known project offers 2 BHK apartments in Kamalgazi, Kolkata South. With a super built-up area, ranging from 885.0 sq. ft. to 890.0 sq. ft., the apartments are available at an economical price range of Rs. 41.59 Lac to Rs. 41.83 Lac.\n\nThere are 3 towers. A close attention has been paid to each detail for a comfortable living. It has Gymnasium, Banquet Hall, 24/7 Power Backup, Indoor Games, CCTV Camera Security, etc. Loaded with all the modern amenities, the project is certainly a steal deal.Residential ApartmentKolkata South2.012.00055.00Symphony Proxima4700.0{'LATITUDE': '22.44497560154', 'LONGITUDE': '88.390239965808'}0887.52 BHK Flat in Kamalgazi['UNDER CONSTRUCTION', 'NEW LAUNCH', 'NEW BOOKING', 'RERA | HIRA']7.0[{'category': 'MetroStation', 'text': '1 Metro Station', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'Shopping', 'text': '1 Shopping', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'Education', 'text': '2 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'Hospital', 'text': '1 Hospital', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Airport', 'text': '1 Airport', 'className': 'aprt', 'icon': 'https://static.99acres.com/universalapp/img/aprt.png'}, {'category': 'RailwayStation', 'text': '1 Railway Station', 'className': 'rail', 'icon': 'https://static.99acres.com/universalapp/img/rail.png'}]Symphony ProximaSymphony Proxima{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '6849', 'LOCALITY_NAME': 'Kamalgazi', 'ADDRESS': None}0.01.0NaNNaNNaNNaNNaNkolkata south3{'LATITUDE': '22.44497560154', 'LONGITUDE': '88.390239965808'}22.44497688.3902401.0220.00.41715
PROP_IDPREFERENCEDESCRIPTIONPROPERTY_TYPECITYTRANSACT_TYPEOWNTYPEBEDROOM_NUMFURNISHFACINGAGETOTAL_FLOORFEATURESPROP_NAMEPRICE_SQFTMAP_DETAILSAMENITIESAREAPROP_HEADINGSECONDARY_TAGSTOTAL_LANDMARK_COUNTFORMATTED_LANDMARK_DETAILSSOCIETY_NAMEBUILDING_NAMElocationBALCONY_NUMFLOOR_NUMCARPET_SQFTSUPERBUILTUP_SQFTBUILTUP_SQFTSUPER_AREASUPERAREA_UNITcitysidelat_longlatitudelongitudesocietypreferencetypeAmenityScorePrice
8750I63225872SThis 4 bhk flat is available for sale in urbana, one of the most prominent projects for residential apartments in em bypass, kolkata south. The floor plan additionally contains 5 bathrooms and 1 balcony. All in all, the apartment is spread over an 3528 sq.Ft.. The apartment building has a total of 45 floors and this property is situated on 10th floor. As the project is already ready to move, so you can easily move into this 1-5 year(s) old property.Residential ApartmentKolkata South1.014.045145.023,24,5,17,6,19,20,21Urbana15022.0{'LATITUDE': '22.548114', 'LONGITUDE': '88.4004968'}5,17,20,21,23,24,6,193528.04 BHK Flat in EM Bypass['READY TO MOVE', 'RESALE', 'RERA | HIRA']15.0[{'category': 'MetroStation', 'text': '1 Metro Station', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'Education', 'text': '2 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'ReligiousPlace', 'text': '1 Religious Place', 'className': 'rlgs', 'icon': 'https://static.99acres.com/universalapp/img/rlgs.png'}, {'category': 'ATM', 'text': '1 ATM', 'className': 'atm', 'icon': 'https://static.99acres.com/universalapp/img/atm_blue.png'}, {'category': 'Hospital', 'text': '1 Hospital', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Airport', 'text': '1 Airport', 'className': 'aprt', 'icon': 'https://static.99acres.com/universalapp/img/aprt.png'}, {'category': 'Bank', 'text': '1 Banks', 'className': 'bnk', 'icon': 'https://static.99acres.com/universalapp/img/bnk.png'}, {'category': 'Park', 'text': '1 Park', 'className': 'prk', 'icon': 'https://static.99acres.com/universalapp/img/prk.png'}, {'category': 'BusStop', 'text': '1 Bus Stop', 'className': 'bus', 'icon': 'https://static.99acres.com/universalapp/img/bus.png'}, {'category': 'RailwayStation', 'text': '1 Railway Station', 'className': 'rail', 'icon': 'https://static.99acres.com/universalapp/img/rail.png'}, {'category': 'Miscellaneous', 'text': '4 Miscellaneouss', 'className': 'oldhme', 'icon': 'https://static.99acres.com/universalapp/img/oldhme.png'}]UrbanaUrbana{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '603', 'LOCALITY_NAME': 'EM Bypass', 'ADDRESS': None}1.010.0NaN3528.0NaNNaNNaNkolkata south3{'LATITUDE': '22.548114', 'LONGITUDE': '88.4004968'}22.54811488.4004975.02261.55.3000
8751G61003098RA very good 2 bhk flat for rent in , kolkata south. It is a superb property and offers an excellent view. The flat is furnished with multiple amenities and promises a comfortable stay. It has a super built-Up area of 1220 sq.Ft. Covered parking.Residential ApartmentKolkata South0.002.013315.023,5,17,6,20,21South City Garden26.0{'LATITUDE': '22.4972', 'LONGITUDE': '88.338401'}5,17,20,21,23,6,1031220.02 BHK Flat in Tara Park['FURNISHED', 'FOR SINGLE MEN', 'FOR SINGLE WOMEN']50.0[{'category': 'MetroStation', 'text': '3 Metro Stations', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'ReligiousPlace', 'text': '2 Religious Places', 'className': 'rlgs', 'icon': 'https://static.99acres.com/universalapp/img/rlgs.png'}, {'category': 'ATM', 'text': '5 ATMs', 'className': 'atm', 'icon': 'https://static.99acres.com/universalapp/img/atm_blue.png'}, {'category': 'Hospital', 'text': '24 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'BusDepot', 'text': '1 Bus Depot', 'className': 'busdpt', 'icon': 'https://static.99acres.com/universalapp/img/busdpt.png'}]South City GardenSouth City Garden{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '32123', 'LOCALITY_NAME': 'Tara Park', 'ADDRESS': None}1.08.0NaN1220.0NaNNaNNaNkolkata south3{'LATITUDE': '22.4972', 'LONGITUDE': '88.338401'}22.49720088.3384013.01246.70.0032
8752E64737128SThis beautiful 3 bhk flat in new alipore, kolkata south is situated in privet, one of the popular residential society in kolkata south. The flat occupies a super built up area of 2000 sq.Ft. That consists of 3 bedrooms, 3 bathrooms and 1 balcony. The property is located on the 2nd floor of a 5 floor tall building. An added advantage of this 10+ year(s) old flat is that it is available for immediate possession as the project is already ready to move.Residential ApartmentKolkata South1.013.04735.023,6,19,20,21privet9000.0{'LATITUDE': '22.5143577', 'LONGITUDE': '88.3251456'}20,21,23,6,19,1032000.03 BHK Flat in New Alipore['READY TO MOVE', 'RESALE']49.0[{'category': 'Shopping', 'text': '1 Shopping', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'ReligiousPlace', 'text': '5 Religious Places', 'className': 'rlgs', 'icon': 'https://static.99acres.com/universalapp/img/rlgs.png'}, {'category': 'ATM', 'text': '5 ATMs', 'className': 'atm', 'icon': 'https://static.99acres.com/universalapp/img/atm_blue.png'}, {'category': 'Hospital', 'text': '21 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Pharmacy', 'text': '1 Pharmacy', 'className': 'phmcy', 'icon': 'https://static.99acres.com/universalapp/img/phmcy.png'}, {'category': 'BusDepot', 'text': '1 Bus Depot', 'className': 'busdpt', 'icon': 'https://static.99acres.com/universalapp/img/busdpt.png'}, {'category': 'Miscellaneous', 'text': '1 Miscellaneous', 'className': 'oldhme', 'icon': 'https://static.99acres.com/universalapp/img/oldhme.png'}]privetprivet{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '1465', 'LOCALITY_NAME': 'New Alipore', 'ADDRESS': 'Block G'}1.02.0NaN2000.0NaNNaNNaNkolkata south3{'LATITUDE': '22.5143577', 'LONGITUDE': '88.3251456'}22.51435888.3251461.02239.71.8000
8753V49700968RBansdroni.3 min walking distance from master da surya sen metro station.Its complex.24 hrs secury,boring,cctv,gym,comunity hall all facility here..4 min walking distance from bansdroni super market,bank, school all near by..Residential ApartmentKolkata South0.002.02614.05,21,6,23,19,12,9Central Enclave18.0{'LATITUDE': '22.500366', 'LONGITUDE': '88.349671'}5,21,6,23,19,9,12,101950.02 BHK Flat in Naktala['FOR SINGLE MEN', 'FOR SINGLE WOMEN']50.0[{'category': 'MetroStation', 'text': '2 Metro Stations', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'ReligiousPlace', 'text': '2 Religious Places', 'className': 'rlgs', 'icon': 'https://static.99acres.com/universalapp/img/rlgs.png'}, {'category': 'ATM', 'text': '1 ATM', 'className': 'atm', 'icon': 'https://static.99acres.com/universalapp/img/atm_blue.png'}, {'category': 'Hospital', 'text': '25 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}]Central EnclaveCentral Enclave{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '1451', 'LOCALITY_NAME': 'Naktala', 'ADDRESS': None}1.03.0750.0950.0800.0NaNNaNkolkata south3{'LATITUDE': '22.500366', 'LONGITUDE': '88.349671'}22.50036688.3496711.01256.60.0018
8754M61561978RAn excellent 2 bhk residential apartment for rent in lake gardens, kolkata south. It is a very good property and has 1 balcony(s) which make the apartment more spacious. It is . We are looking for rs. 22000.00. It is on the 2nd floor . Open parking. Covered parking.Residential ApartmentKolkata South0.002.01724.017Apartment25.0{'LATITUDE': '22.5050813878', 'LONGITUDE': '88.3544763969'}17,103850.02 BHK Flat in Lake Gardens['FURNISHED', 'FOR SINGLE MEN', 'FOR SINGLE WOMEN']50.0[{'category': 'MetroStation', 'text': '1 Metro Station', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'ReligiousPlace', 'text': '2 Religious Places', 'className': 'rlgs', 'icon': 'https://static.99acres.com/universalapp/img/rlgs.png'}, {'category': 'ATM', 'text': '1 ATM', 'className': 'atm', 'icon': 'https://static.99acres.com/universalapp/img/atm_blue.png'}, {'category': 'Hospital', 'text': '24 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}]ApartmentApartment{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '1445', 'LOCALITY_NAME': 'Lake Gardens', 'ADDRESS': None}1.02.0NaN850.0NaNNaNNaNkolkata south3{'LATITUDE': '22.5050813878', 'LONGITUDE': '88.3544763969'}22.50508188.3544763.0128.10.0022
8755J66826540SAmbuja upohar the condoville is one of the most popular destination for buying apartments/ flats in chak garia, kolkata south. You too can be a part of this society by purchasing this 3 bhk flat here. The floor plan additionally contains 3 bedroom(s), 2 bathrooms and 2 balconies. All in all, the flat is spread over a super built up area of 1843 sq.Ft. The flat has a total of 19 floors and this property is situated on 14th floor. Being a ready to move project, you can expect immediate possession of this 5-10 years old property.Residential ApartmentKolkata South1.013.047219.023,24,5,17,6,19,20,21Ambuja Upohar The Condoville8410.0{'LATITUDE': '22.485966', 'LONGITUDE': '88.396453'}5,17,20,21,23,24,6,191843.03 BHK Flat in Chak Garia['READY TO MOVE', 'RESALE']49.0[{'category': 'MetroStation', 'text': '1 Metro Station', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'ReligiousPlace', 'text': '14 Religious Places', 'className': 'rlgs', 'icon': 'https://static.99acres.com/universalapp/img/rlgs.png'}, {'category': 'ATM', 'text': '3 ATMs', 'className': 'atm', 'icon': 'https://static.99acres.com/universalapp/img/atm_blue.png'}, {'category': 'Hospital', 'text': '13 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Pharmacy', 'text': '1 Pharmacy', 'className': 'phmcy', 'icon': 'https://static.99acres.com/universalapp/img/phmcy.png'}, {'category': 'BusDepot', 'text': '1 Bus Depot', 'className': 'busdpt', 'icon': 'https://static.99acres.com/universalapp/img/busdpt.png'}, {'category': 'Miscellaneous', 'text': '1 Miscellaneous', 'className': 'oldhme', 'icon': 'https://static.99acres.com/universalapp/img/oldhme.png'}, {'category': 'Library', 'text': '1 Library', 'className': 'lib', 'icon': 'https://static.99acres.com/universalapp/img/lib.png'}]Ambuja Upohar The CondovilleAmbuja Upohar The Condoville{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '32163', 'LOCALITY_NAME': 'Chak Garia', 'ADDRESS': None}2.014.0NaN1843.0NaNNaNNaNkolkata south3{'LATITUDE': '22.485966', 'LONGITUDE': '88.396453'}22.48596688.3964533.02261.51.5500
8756E66826562SLooking for a 3 bhk property for sale in kolkata south? Buy this 3 bhk flat in ambuja upohar the condoville that is situated in chak garia, kolkata south. The floor plan additionally contains 3 bedroom(s), 3 bathrooms and 2 balconies. All in all, the flat is spread over a super built up area of 2079 sq.Ft. The property is located on the 10th floor of a 19 floors tall building. As the project is already ready to move, so you can easily move into this 5-10 years old property.Residential ApartmentKolkata South1.013.047219.023,24,5,17,6,19,20,21Ambuja Upohar The Condoville8417.0{'LATITUDE': '22.485966', 'LONGITUDE': '88.396453'}5,17,20,21,23,24,6,192079.03 BHK Flat in Chak Garia['READY TO MOVE', 'RESALE']49.0[{'category': 'MetroStation', 'text': '1 Metro Station', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'ReligiousPlace', 'text': '14 Religious Places', 'className': 'rlgs', 'icon': 'https://static.99acres.com/universalapp/img/rlgs.png'}, {'category': 'ATM', 'text': '3 ATMs', 'className': 'atm', 'icon': 'https://static.99acres.com/universalapp/img/atm_blue.png'}, {'category': 'Hospital', 'text': '13 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Pharmacy', 'text': '1 Pharmacy', 'className': 'phmcy', 'icon': 'https://static.99acres.com/universalapp/img/phmcy.png'}, {'category': 'BusDepot', 'text': '1 Bus Depot', 'className': 'busdpt', 'icon': 'https://static.99acres.com/universalapp/img/busdpt.png'}, {'category': 'Miscellaneous', 'text': '1 Miscellaneous', 'className': 'oldhme', 'icon': 'https://static.99acres.com/universalapp/img/oldhme.png'}, {'category': 'Library', 'text': '1 Library', 'className': 'lib', 'icon': 'https://static.99acres.com/universalapp/img/lib.png'}]Ambuja Upohar The CondovilleAmbuja Upohar The Condoville{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '32163', 'LOCALITY_NAME': 'Chak Garia', 'ADDRESS': None}2.010.0NaN2079.01875.0NaNNaNkolkata south3{'LATITUDE': '22.485966', 'LONGITUDE': '88.396453'}22.48596688.3964533.02261.51.7500
8757J71210104SIt is a standard 2 bhk with 2 room, 2 bath, 1 big balcony, east garden swimming pool facing flat on lower floor, 1 basement parking, ready to move property for immediate sell.Residential ApartmentKolkata East1.012.023217.033,23,12,24,47,25,26,17,39,29,19,1,3,5,6,9,40,41,20,31,21,32DLF New Town Heights5887.0{'LATITUDE': '22.6006912', 'LONGITUDE': '88.4694535'}17,5,20,21,32,23,24,47,39,29,19,1,6,9,40,41,33,12,25,26,3,31,101,1021257.02 BHK Flat in New Town['READY TO MOVE', 'RESALE']35.0[{'category': 'ReligiousPlace', 'text': '2 Religious Places', 'className': 'rlgs', 'icon': 'https://static.99acres.com/universalapp/img/rlgs.png'}, {'category': 'ATM', 'text': '3 ATMs', 'className': 'atm', 'icon': 'https://static.99acres.com/universalapp/img/atm_blue.png'}, {'category': 'Hospital', 'text': '5 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'OfficeComplex', 'text': '4 Office Complexes', 'className': 'ofce', 'icon': 'https://static.99acres.com/universalapp/img/ofce.png'}, {'category': 'Parking', 'text': '1 Parking', 'className': 'prkng', 'icon': 'https://static.99acres.com/universalapp/img/prkng.png'}, {'category': 'BusDepot', 'text': '2 Bus Depots', 'className': 'busdpt', 'icon': 'https://static.99acres.com/universalapp/img/busdpt.png'}, {'category': 'Miscellaneous', 'text': '1 Miscellaneous', 'className': 'oldhme', 'icon': 'https://static.99acres.com/universalapp/img/oldhme.png'}]DLF New Town HeightsDLF New Town Heights{'CITY': '28', 'CITY_NAME': 'Kolkata East', 'LOCALITY_ID': '1503', 'LOCALITY_NAME': 'New Town', 'ADDRESS': None}1.03.0900.01257.01050.0NaNNaNkolkata east1{'LATITUDE': '22.6006912', 'LONGITUDE': '88.4694535'}22.60069188.4694544.022130.50.7400
8758I41581465RA very good 3 bhk flat for rent in south city, prince anwar shah rd, kolkata south. It is a superb property and offers an excellent view. The flat is furnished with multiple amenities and promises a comfortable stay. It is feng shui/vaastu compliant, which is considered to bring positive energy. Indeed, the society too has multiple facilities for enjoyment, such as club house/community center, fitness centre/gym and swimming pool etc. It has a super built-Up area of 1458 sq. Ft. Other facilities include lift(S), park, visitor parking, water storage, intercom facility and security/fire alarm etc. It also has vitrified flooring. South-East facing. 1 covered parking. Full power back up.Residential ApartmentKolkata South0.003.017135.03,12,21,1,6,23,19,24,5,20,17South City37.0{'LATITUDE': '22.499905', 'LONGITUDE': '88.360697'}21,5,20,17,1,6,23,19,24,3,12,1031458.03 BHK Flat in Prince Anwar Shah Rd['FURNISHED', 'FOR SINGLE MEN', 'FOR SINGLE WOMEN']15.0[{'category': 'Shopping', 'text': '2 Shoppings', 'className': 'shpng', 'icon': 'https://static.99acres.com/universalapp/img/shpng.png'}, {'category': 'Education', 'text': '5 Educations', 'className': 'schl', 'icon': 'https://static.99acres.com/universalapp/img/schl.png'}, {'category': 'ATM', 'text': '1 ATM', 'className': 'atm', 'icon': 'https://static.99acres.com/universalapp/img/atm_blue.png'}, {'category': 'Hospital', 'text': '1 Hospital', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}, {'category': 'Bank', 'text': '2 Bankss', 'className': 'bnk', 'icon': 'https://static.99acres.com/universalapp/img/bnk.png'}, {'category': 'Park', 'text': '1 Park', 'className': 'prk', 'icon': 'https://static.99acres.com/universalapp/img/prk.png'}, {'category': 'Miscellaneous', 'text': '3 Miscellaneouss', 'className': 'oldhme', 'icon': 'https://static.99acres.com/universalapp/img/oldhme.png'}]South CitySouth City{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '4316', 'LOCALITY_NAME': 'Prince Anwar Shah Rd', 'ADDRESS': 'Pince Anwar Shah Road'}1.024.0NaN1458.0NaNNaNNaNkolkata south3{'LATITUDE': '22.499905', 'LONGITUDE': '88.360697'}22.49990588.3606974.01288.20.0055
8759E67621228RExplore this amicable adhunika chs of lake gardens in kolkata south ! Live in a 2 bhk flat for rent in lake gardens. Property has 800 sq.Ft. Carpet area with 2 bathrooms & 1 balcony attached. The flat is built on 800 sq.Ft. Area. Ease of access to 1 open parking within society. This flat is situated at 2nd in a tower with 3 floors. This is a 10+ years old society. This is a furnished flat. Furnishing options provided - 1 ac, 1 chimney, 1 curtains, 1 dining table, 1 exhaust fan, 2 fan, 1 geyser, 3 light, 1 modular kitchen, 1 fridge, 1 sofa, 1 stove, 1 study table, 1 tv, 2 wardrobe, 1 washing machine and 1 water purifier. Property will be leased from tue, 28 feb 2023 to families, single men and single women only with a notice of 1 month. Expected rent for this property is 25,000 monthly. Electricity & water charges to be paid by tenant on monthly basis to authorities. A one-Time payment of 75000 is required by the owner as a security deposit.Residential ApartmentKolkata South0.002.01333.023,19Adhunika CHS31.0{'LATITUDE': '22.5050813878', 'LONGITUDE': '88.3544763969'}23,19,103800.02 BHK Flat in Lake Gardens['FURNISHED', 'FOR SINGLE MEN', 'FOR SINGLE WOMEN']50.0[{'category': 'MetroStation', 'text': '1 Metro Station', 'className': 'mstn', 'icon': 'https://static.99acres.com/universalapp/img/mstn.png'}, {'category': 'ReligiousPlace', 'text': '2 Religious Places', 'className': 'rlgs', 'icon': 'https://static.99acres.com/universalapp/img/rlgs.png'}, {'category': 'ATM', 'text': '1 ATM', 'className': 'atm', 'icon': 'https://static.99acres.com/universalapp/img/atm_blue.png'}, {'category': 'Hospital', 'text': '24 Hospitals', 'className': 'hsptl', 'icon': 'https://static.99acres.com/universalapp/img/hsptl.png'}]Adhunika CHSAdhunika CHS{'CITY': '27', 'CITY_NAME': 'Kolkata South', 'LOCALITY_ID': '1445', 'LOCALITY_NAME': 'Lake Gardens', 'ADDRESS': None}1.02.0800.0NaNNaNNaNNaNkolkata south3{'LATITUDE': '22.5050813878', 'LONGITUDE': '88.3544763969'}22.50508188.3544761.01214.40.0025